Part-based recognition is expected to be robust in difficult handwritten character recognition tasks. This is because part-based recognition is based on aggregation of independent recognition results at individual local parts without considering their global relations and thus is robust against various deformations, such as partial occlusion, overlap, broken stroke, etc. Since part-based recognition is a new approach, there are still several open problems toward its practical use. For example, compared with entire images, local parts are more ambiguous, i.e., less discriminative. For better recognition accuracy and less computations, we need to know the characteristics of local parts and then, for example, discard less discriminative parts. The purpose of this paper is to conduct some experiments in order to observe and analyze how the local parts of multiple classes are distributed in feature spaces. By handling parts appropriately based on the analysis, we will be able to enhance the usefulness of the part-based method.