Protein molecules are combined with another ones which have similar shapes at pocket positions. The pocket positions can be good references to describe the shapes of protein molecules. Harris corner detector is commonly used to detect feature points of 2 or 3D objects. Feature points can be found on the pocket areas and the points which have high derivatives. Generally speaking, the densities of feature points are relatively high at pocket areas because the shapes of pockets are concave. The pocket areas can be decided by the subdivision of voxel cubes which include feature points. The Euclidean distances between feature points and the central coordinate of the decided pocket area are calculated and sorted. The graph of sorted distances describes the shape of a protein molecule and the distribution of feature points. Therefore, it can be used to classify protein molecules by their shapes. Even though the shapes of protein molecules have been distorted with noises, they can be recognized with the accuracy more than 95 %. The accurate shape recognition provides the information to predict the binding properties of protein molecules.