An invariant handwritten Chinese character recognition system is proposed. Characters can be in arbitrary location, scale and orientation. Five invariant features are employed in this study. The first four features are used for preclassification to reduce matching time. The last feature, ring data, constructs ring-data vectors to characterize character samples and constructs weighted ring-data matrices to characterize characters to further reduce matching time. Fuzzy membership functions are defined based on these two characteristics to match characters. A character set is constructed from 200 handwritten Chinese characters and comprising several different samples of each character in arbitrary orientations. The performance of the proposed invariant features and fuzzy matching is verified through extensive experiments with the character set: (i) the performance of the proposed fuzzy matching is superior to that of two traditional statistical classifiers; (ii) the performance of the fuzzy ring-data vector is clearly superior to that of the fuzzy ring-data matrix, but the latter needed less matching time; (iii) the preclassification reduces the fuzzy matching time and improves the recognition rate; and (iv) the performance of the proposed invariant features is clearly superior to that of moment invariants.