In the part-based recognition method proposed in this paper, a handwritten character image is represented by just a set of local parts. Then, each local part of the input pattern is recognized by a nearest-neighbor classifier. Finally, the category of the input pattern is determined by aggregating the local recognition results. This approach is opposed to conventional character recognition approaches which try to benefit from the global structure information as much as possible. Despite a pessimistic expectation, we have reached recognition rates much higher than 90% for a digit recognition task. In this paper we provide a detailed analysis in order to understand the results and find the merits of the local approach.