One of the aims of the Protein Structure Initiative (PSI) in the post genome-sequencing era is to elucidate biochemical and biophysical functions of each protein structure. Thus, the development of new methods for a large-scale analysis/annotation of protein functional residues is inevitable. Currently existing methods are not capable to do so due to the lack of automation, availability, and/or poor performance. In our previous work we were able to improve the accuracy of the prediction to ~86%, although the number of false-positives remained high. In this paper we present a fully-automated method for the prediction of catalytic residues in proteins that improves accuracy by reduction of false-positives, and is applicable for a large-scale analysis. Here, catalytic residues are predicted by machine learning approach followed by hierarchical analysis of the predicted residues. The capability of the method was tested on diverse family of hydrolytic enzymes with a/b hydrolase fold with widely differing phylogenetic origins and catalytic functions. The method was executed manually and then fully reproduces automatically. In the manual analysis, in 17 enzymes, the method correctly predicted all 3 residues of the catalytic triad with 3 false-positives out of 282 residues on average. Our method successfully eliminates the number of false-positives, while being applicable for a large-scale analysis of the protein function.