diff --git a/.gitignore b/.gitignore
index fcd11ea..4fe82f1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 *.pdf
 *.json
+*.png
 __pycache__/
 testStatements/
diff --git a/CLAUDE.md b/CLAUDE.md
index 5b058a9..e4f77b3 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -4,11 +4,14 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Project Overview
 
-Single-script Python tool that extracts credit card transactions from BAC Costa Rica statement PDFs. Parses sections B (purchases), D (other charges), and E (voluntary services) and outputs JSON.
+Python tools for BAC Costa Rica credit card statement processing:
+- `bac_extract.py`: Extracts transactions from statement PDFs to JSON
+- `bac_analyze.py`: Analyzes JSON output with categorization and graphs
 
 ## Dependencies
 
-- pdfplumber (>=0.10.0)
+- pdfplumber (>=0.10.0) - PDF extraction
+- matplotlib (>=3.5.0) - graphs (optional, only for bac_analyze.py --graph)
 
 ## Commands
 
@@ -16,22 +19,22 @@ Single-script Python tool that extracts credit card transactions from BAC Costa
 # Run tests
 python testStatements/run_tests.py
 
-# Run extractor
-python bac_extract.py <pdf_file> [options]
-
-# Examples
-python bac_extract.py EstadodeCuenta.pdf --pretty
+# Extract transactions from PDF
+python bac_extract.py statement.pdf --pretty
 python bac_extract.py statement.pdf -o output.json -v
-```
 
-Options:
-- `-o, --output`: Output JSON path (default: transactions.json)
-- `--pretty`: Pretty-print JSON
-- `-v, --verbose`: Enable debug logging
+# Analyze transactions (supports multiple JSON files)
+python bac_analyze.py transactions.json
+python bac_analyze.py *.json --graph all
+python bac_analyze.py *.json --graph bar -o spending.png
+python bac_analyze.py *.json --categories my_categories.json
+```
 
 ## Architecture
 
-The extraction pipeline:
+### bac_extract.py
+
+Extraction pipeline:
 1. Validates PDF is a BAC statement (`is_bac_statement`)
 2. Iterates pages line-by-line, detecting section boundaries via `SECTIONS` dict patterns
 3. Parses transactions matching `TRANSACTION_PATTERN` regex
@@ -41,7 +44,10 @@ Key data structures:
 - `SECTIONS`: Maps section IDs (B/D/E) to start/end regex patterns and output keys
 - `SPANISH_MONTHS`: Spanish month abbreviations for date parsing
 
-Key parsing functions:
-- `parse_spanish_date`: Converts "15-ENE-25" to "2025-01-15"
-- `parse_amount`: Handles "1,234.56" and trailing negatives "100.00-"
-- `matches_patterns`: Generic regex pattern matcher for section detection
+### bac_analyze.py
+
+Analysis pipeline:
+1. Loads transactions from one or more JSON files (purchases only)
+2. Categorizes by matching description against patterns in `categories.json`
+3. Aggregates by category and month, keeping CRC/USD separate
+4. Outputs text summary and optional graphs (bar/pie/timeline/all)
diff --git a/README.md b/README.md
index a56332a..709c00d 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,16 @@
-# BAC Statement Extractor
+# BAC Statement Tools
 
-Extracts credit card transactions from BAC Costa Rica statement PDFs. Parses sections B (purchases), D (other charges), and E (voluntary services) and outputs JSON.
+Tools for processing BAC Costa Rica credit card statement PDFs.
 
 ## Dependencies
 
 - Python 3.10+
 - pdfplumber (>=0.10.0)
+- matplotlib (>=3.5.0) - optional, for graphs
 
-## Usage
+## Extraction
+
+Extract transactions from statement PDFs to JSON.
 
 ```bash
 python bac_extract.py <pdf_file> [options]
@@ -24,6 +27,56 @@ python bac_extract.py statement.pdf --pretty
 python bac_extract.py statement.pdf -o output.json -v
 ```
 
+## Analysis
+
+Analyze extracted transactions with category breakdowns and graphs.
+
+```bash
+python bac_analyze.py <json_files...> [options]
+```
+
+**Options:**
+- `--graph {bar,pie,timeline,all}`: Generate graph(s)
+- `-o, --output`: Output file for graph (default: spending_<type>.png)
+- `--show`: Display graph interactively
+- `--categories`: Custom categories file (default: categories.json)
+
+**Examples:**
+```bash
+# Text summary
+python bac_analyze.py transactions.json
+
+# Analyze multiple statements
+python bac_analyze.py *.json
+
+# Generate all graphs
+python bac_analyze.py *.json --graph all
+
+# Generate bar chart with custom output
+python bac_analyze.py *.json --graph bar -o spending.png
+
+# Use custom categories
+python bac_analyze.py *.json --categories my_categories.json
+```
+
+## Categories
+
+Create a `categories.json` file to customize spending categories. Each category maps to a list of merchant name patterns (case-insensitive substring match).
+
+```json
+{
+  "Groceries": ["SUPERMARKET", "WALMART", "FRESH MARKET"],
+  "Gas": ["SERVICENTRO", "DELTA", "SHELL"],
+  "Restaurants": ["RESTAURANT", "CAFE", "PIZZA", "SUSHI"],
+  "Transportation": ["UBER", "TAXI", "PARKING"],
+  "Entertainment": ["CINEMA", "NETFLIX", "STEAM"],
+  "Utilities": ["ELECTRIC", "WATER", "INTERNET"],
+  "Subscriptions": ["SPOTIFY", "YOUTUBE", "CHATGPT"]
+}
+```
+
+Transactions not matching any pattern are categorized as "Other".
+
 ## Output Format
 
 ```json
diff --git a/bac_analyze.py b/bac_analyze.py
new file mode 100755
index 0000000..c3808c0
--- /dev/null
+++ b/bac_analyze.py
@@ -0,0 +1,251 @@
+#!/usr/bin/env python3
+"""
+BAC Spending Analysis Tool
+
+Analyzes transaction JSON output from bac_extract.py.
+Provides spending categorization and visualization.
+"""
+
+import argparse
+import json
+import sys
+from collections import defaultdict
+from pathlib import Path
+
+try:
+    import matplotlib.pyplot as plt
+    HAS_MATPLOTLIB = True
+except ImportError:
+    HAS_MATPLOTLIB = False
+
+
+def load_transactions(json_files: list[Path]) -> list[dict]:
+    """Load and merge transactions from multiple JSON files."""
+    transactions = []
+    for path in json_files:
+        with open(path, encoding="utf-8") as f:
+            data = json.load(f)
+        # Only include purchases, skip other_charges and voluntary_services
+        transactions.extend(data.get("purchases", []))
+    return transactions
+
+
+def load_categories(path: Path) -> dict[str, list[str]]:
+    """Load category patterns from JSON file."""
+    with open(path, encoding="utf-8") as f:
+        return json.load(f)
+
+
+def categorize(description: str, categories: dict[str, list[str]]) -> str:
+    """Return category for a transaction description."""
+    desc_upper = description.upper()
+    for category, patterns in categories.items():
+        for pattern in patterns:
+            if pattern.upper() in desc_upper:
+                return category
+    return "Other"
+
+
+def aggregate_by_category(
+    transactions: list[dict], categories: dict[str, list[str]]
+) -> dict[str, dict[str, float]]:
+    """Sum spending per category, separate CRC/USD."""
+    result = defaultdict(lambda: {"crc": 0.0, "usd": 0.0})
+    for txn in transactions:
+        cat = categorize(txn["description"], categories)
+        if txn["amount_crc"]:
+            result[cat]["crc"] += txn["amount_crc"]
+        if txn["amount_usd"]:
+            result[cat]["usd"] += txn["amount_usd"]
+    return dict(result)
+
+
+def aggregate_by_month(transactions: list[dict]) -> dict[str, dict[str, float]]:
+    """Sum spending per month (YYYY-MM), separate CRC/USD."""
+    result = defaultdict(lambda: {"crc": 0.0, "usd": 0.0})
+    for txn in transactions:
+        month = txn["date"][:7]  # YYYY-MM
+        if txn["amount_crc"]:
+            result[month]["crc"] += txn["amount_crc"]
+        if txn["amount_usd"]:
+            result[month]["usd"] += txn["amount_usd"]
+    return dict(result)
+
+
+def print_summary(by_category: dict, by_month: dict):
+    """Print text summary to stdout."""
+    print("=== Spending by Category ===")
+
+    # Sort by CRC amount descending
+    sorted_cats = sorted(by_category.items(), key=lambda x: x[1]["crc"], reverse=True)
+    total_crc = 0.0
+    total_usd = 0.0
+
+    for cat, amounts in sorted_cats:
+        crc, usd = amounts["crc"], amounts["usd"]
+        total_crc += crc
+        total_usd += usd
+        print(f"{cat:20} CRC {crc:>12,.2f}    USD {usd:>8,.2f}")
+
+    print("-" * 50)
+    print(f"{'Total':20} CRC {total_crc:>12,.2f}    USD {total_usd:>8,.2f}")
+
+    print("\n=== Monthly Spending ===")
+    for month in sorted(by_month.keys()):
+        amounts = by_month[month]
+        print(f"{month}:             CRC {amounts['crc']:>12,.2f}    USD {amounts['usd']:>8,.2f}")
+
+
+def plot_bar(data: dict, output: Path, show: bool):
+    """Bar chart of category spending (CRC)."""
+    # Sort by amount descending
+    sorted_items = sorted(data.items(), key=lambda x: x[1]["crc"], reverse=True)
+    categories = [item[0] for item in sorted_items]
+    amounts = [item[1]["crc"] for item in sorted_items]
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    bars = ax.barh(categories, amounts, color="steelblue")
+    ax.set_xlabel("Amount (CRC)")
+    ax.set_title("Spending by Category")
+    ax.invert_yaxis()
+
+    # Add value labels
+    for bar, amount in zip(bars, amounts):
+        ax.text(bar.get_width() + max(amounts) * 0.01, bar.get_y() + bar.get_height() / 2,
+                f"{amount:,.0f}", va="center", fontsize=9)
+
+    plt.tight_layout()
+    plt.savefig(output, dpi=150)
+    print(f"Saved bar chart to {output}")
+
+    if show:
+        plt.show()
+    plt.close()
+
+
+def plot_pie(data: dict, output: Path, show: bool):
+    """Pie chart of category distribution (CRC)."""
+    # Filter out zero/negative and sort
+    filtered = {k: v["crc"] for k, v in data.items() if v["crc"] > 0}
+    sorted_items = sorted(filtered.items(), key=lambda x: x[1], reverse=True)
+
+    categories = [item[0] for item in sorted_items]
+    amounts = [item[1] for item in sorted_items]
+
+    fig, ax = plt.subplots(figsize=(10, 8))
+    wedges, texts, autotexts = ax.pie(
+        amounts, labels=categories, autopct="%1.1f%%",
+        startangle=90, pctdistance=0.75
+    )
+    ax.set_title("Spending Distribution by Category (CRC)")
+
+    plt.tight_layout()
+    plt.savefig(output, dpi=150)
+    print(f"Saved pie chart to {output}")
+
+    if show:
+        plt.show()
+    plt.close()
+
+
+def plot_timeline(data: dict, output: Path, show: bool):
+    """Line chart of monthly spending (CRC)."""
+    months = sorted(data.keys())
+    amounts = [data[m]["crc"] for m in months]
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.plot(months, amounts, marker="o", linewidth=2, markersize=8, color="steelblue")
+    ax.fill_between(months, amounts, alpha=0.3, color="steelblue")
+
+    ax.set_xlabel("Month")
+    ax.set_ylabel("Amount (CRC)")
+    ax.set_title("Monthly Spending")
+    ax.tick_params(axis="x", rotation=45)
+
+    # Add value labels
+    for month, amount in zip(months, amounts):
+        ax.annotate(f"{amount:,.0f}", (month, amount),
+                    textcoords="offset points", xytext=(0, 10),
+                    ha="center", fontsize=9)
+
+    plt.tight_layout()
+    plt.savefig(output, dpi=150)
+    print(f"Saved timeline chart to {output}")
+
+    if show:
+        plt.show()
+    plt.close()
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Analyze spending from BAC transaction JSON files"
+    )
+    parser.add_argument(
+        "json_files", type=Path, nargs="+",
+        help="JSON files from bac_extract.py"
+    )
+    parser.add_argument(
+        "--graph", choices=["bar", "pie", "timeline", "all"],
+        help="Generate graph type (use 'all' for all graphs)"
+    )
+    parser.add_argument(
+        "-o", "--output", type=Path,
+        help="Output file for graph (default: spending_<type>.png)"
+    )
+    parser.add_argument(
+        "--show", action="store_true",
+        help="Display graph interactively"
+    )
+    parser.add_argument(
+        "--categories", type=Path, default=Path("categories.json"),
+        help="Custom categories file (default: categories.json)"
+    )
+    args = parser.parse_args()
+
+    # Validate input files
+    for path in args.json_files:
+        if not path.exists():
+            sys.exit(f"Error: File not found: {path}")
+
+    # Check matplotlib early if graph requested
+    if args.graph and not HAS_MATPLOTLIB:
+        sys.exit("Error: matplotlib is required for graphs. Install with: pip install matplotlib")
+
+    # Load categories
+    if not args.categories.exists():
+        sys.exit(f"Error: Categories file not found: {args.categories}")
+    categories = load_categories(args.categories)
+
+    # Load transactions
+    transactions = load_transactions(args.json_files)
+    if not transactions:
+        sys.exit("Error: No transactions found in input files")
+
+    # Aggregate data
+    by_category = aggregate_by_category(transactions, categories)
+    by_month = aggregate_by_month(transactions)
+
+    # Print summary
+    print_summary(by_category, by_month)
+
+    # Generate graph if requested
+    if args.graph:
+        if args.graph == "all":
+            prefix = args.output.stem if args.output else "spending"
+            suffix = args.output.suffix if args.output else ".png"
+            plot_bar(by_category, Path(f"{prefix}_bar{suffix}"), args.show)
+            plot_pie(by_category, Path(f"{prefix}_pie{suffix}"), args.show)
+            plot_timeline(by_month, Path(f"{prefix}_timeline{suffix}"), args.show)
+        else:
+            output = args.output or Path(f"spending_{args.graph}.png")
+            if args.graph == "bar":
+                plot_bar(by_category, output, args.show)
+            elif args.graph == "pie":
+                plot_pie(by_category, output, args.show)
+            elif args.graph == "timeline":
+                plot_timeline(by_month, output, args.show)
+
+
+if __name__ == "__main__":
+    main()