Accurate protein function prediction is an important subject in bioinformatics, especially where sequentially and structurally similar proteins have different functions. Malate dehydrogenase and L-lactate dehydrogenase are two evolutionary related enzymes, which exist in a wide variety of organisms. These enzymes are sequentially and structurally similar and share common active site residues, spatial patterns and molecular mechanisms. Here, we study various features of the active site cavity of 229 PDB chain entries and try to classify them automatically by various classifiers including the support vector machine, k nearest neighbour and random forest methods. The results show that the support vector machine yields the highest predictive performance among mentioned classifiers. Despite very close and conserved patterns among Malate dehydrogenases and L-lactate dehydrogenases, the SVM predicts the function efficiently and achieves 0.973 Matthew's correlation coefficient and 0.987 F-score. The same approach can be used in other enzyme families for automatic discrimination between homologous enzymes with common active site elements, however, acting on different substrates.