Writer recognition is very important for man-machine interface and security. Because each writer has a particular writing form, it is possible to use handwriting characters for writer recognition But a handwriting character is not stable; it changes time to time. This makes writer recognition difficult. To overcome this difficulty, we apply fuzzy theory, because it can absorb the instability of handwriting characters by membership functions. We propose a new method to recognize waters in a short time with a simple algorithm. Three types of membership functions are obtained from normalized hand-writing characters. Three methods, affine transformation (AT), line density equalization (LDE)[1], and four-parts divided affine transformation (FPDAT), are used for normalization. Two types of similarity evaluation functions-ordinary summation of overlapped membership function (SOM), and summation of overlapped and subtraction of un-overlapped membership function (SOSUM)-are tested for 11 Chinese characters written by 16 persons. After evaluating the recognition ratios, we propose the combination of SOSUM and FPDAT. The recognition ratio by this method is 87.9%.