In this study we present an automatic on-line adaptation mechanism to the writer's handwriting style for the recognition of isolated handwritten characters. The classifier we use here is based on a Fuzzy Inference System (FIS) similar to those we have developed for handwriting recognition but simplified for this study. Doing so, the adaptation mechanisms presented here can be transposed to the original systems. In this FIS each premise rule is composed of a fuzzy prototype which represents intrinsic properties of a class. The consequent part of rules associates a score to the prototype for each class. The adaptation mechanism affects both the conclusions of the rules and the fuzzy prototypes by re-centering them thanks to a new approach inspired by the Learning Vector Quantization. Doing so, the FIS is automatically fitted to the handwriting style of the writer that currently uses the system. The adaptation mechanisms were tested with 8 different writers and the results illustrate the benefits of the method in term of error rate reduction (up to 80% and 69% in average). This allows such kind of simple classifiers to achieve up to 97.7% of recognition accuracy on the 26 Latin letters.