This paper investigates a part-based recognition method of handwritten digits. In the proposed method, the global structure of digit patterns is discarded by representing each pattern by just a set of local feature vectors. The method is then comprised of two steps. First, each of J local feature vectors of a target pattern is recognized into one of ten categories ("0"-"9") by the nearest neighbor discrimination with a large database of reference vectors. Second, the category of the target pattern is determined by the majority voting on the J local recognition results. Despite a pessimistic expectation, we have reached recognition rates much higher than 90% for the task of digit recognition.