In this paper, a simple and effective approach to the coarse classification of handwritten Chinese characters is proposed. In our approach, a Chinese character is characterized by string representation using periphery and global feature vectors. The peripheral features include four strings to represent the structure of segments in top, bottom, left, and right directions. The global features include the number of horizontal segments in the top direction and bottom direction, and the number of stroke segments in a character. In addition, a scoring-based coarse classification scheme is devised in choosing the proper candidate characters. Twenty sets of Chinese characters (5401 characters/set) are tested. The number of candidate characters is reduced from 5401 to about 80 with the error rate less than 1.2% in average. Experimental results reveal the feasibility of the proposed approach in classifying Chinese characters. (C) 1997 Pattern Recognition Society.