The increase in known three-dimensional protein structures enables us to build statistical profiles of important functional sites in protein molecules. These profiles can then be used to recognize sites in large-scale automated annotations of new protein structures. We report an improved FEATURE system which recognizes functional sites in protein structures. FEATURE defines multi-level physico-chemical properties and recognizes sites based on the spatial distribution of these properties in the sites' microenvironments. It uses a Bayesian scoring function to compare a query region with the statistical profile built from known examples of sites and control nonsites. We have previously shown that FEATURE can accurately recognize calcium-binding sites and have reported interesting results scanning for calcium-binding sites in the entire Protein Data Bank. Here we report the ability of the improved FEATURE to characterize and recognize geometrically complex and asymmetric sites such as ATP-binding sites and disulfide bond-forming sites. FEATURE does not rely on conserved residues or conserved residue geometry of the sites. We also demonstrate that, in the absence of a statistical profile of the sites, FEATURE can use an artificially constructed profile based on a priori knowledge to recognize the sites in new structures, using redoxin active sites as an example.