Knowledge of the three-dimensional structure of ligand binding sites in proteins provides valuable information for computer-assisted drug design. We present a method for the automated extraction and classification of ligand binding site topologies, in which protein surface cavities are represented as branched frameworks. The procedure employs a growing neural gas approach for pocket topology assignment and pocket framework generation. We assessed the structural diversity of 623 known ligand binding site topologies based on framework cluster analysis. At a resolution of 5 A only 23 structurally distinct topology groups were formed; this suggests an overall limited structural diversity of ligand-accommodating protein cavities. Higher resolution allowed for identification of protein-family specific pocket features. Pocket frameworks highlight potentially preferred modes of ligand-receptor interactions and will help facilitate the identification of druggable subpockets suitable for ligand affinity and selectivity optimization.