An automatic off-line character recognition system for totally unconstrained handwritten strokes is presented. A stroke representation is developed and described using five types of feature. Fuzzy state machines are defined to work as recognizers of strokes. An algorithm to obtain a deterministic fuzzy state machine from a stroke representation, that is capable of recognizing that stroke and its variants is presented. An algorithm is developed to merge two fuzzy state machines into one machine. The use of fuzzy machines to recognize strokes is clarified through a recognition algorithm. The learning algorithm is a complex of the previous algorithms. A set of 20 stroke classes was used in the learning and recognition stages. The system was trained on 5890 unnormalized strokes written by five writers. The learning stage produced a fuzzy state machine of 2705 states and 8640 arcs. A total of 6865 unnormalized strokes, written freely by five writers other than the writers of the learning stage, was used in testing. The recognition, rejection and error rates were 94.8%, 1.2% and 4.0%, respectively. The system can be more developed to deal with cursive handwriting.