Abstract: At present, pattern classification systems based on neural network are being widely used in many fields. However these systems utilize the off line adaptation, i.e., each time new information is added to the systems, it requires a complete retaining of the systems with both the old and the new information. As such, the off line adaptation can lead to increasingly longer training time. For the overlapping classes, the most prevalent method of minimizing misclassification is the construction of a Bayes classifier. Unfortunately, to build a Bayes classifier requires knowledge of the underlying probability density function for each class. This is the information that is quite unavailable. In order to solve these problems, according to the fuzzy set theory, a kind of pattern classification method based on fuzzy neural network is presented in the paper. This method combines fuzzy logic with neural network. The neural network consisting of different kinds of nerons carries out the logic operations AND, OR and MATCH widely applied in fuzzy sets so as to increase the on line adaptation, overlapping classes, learning efficiency and interpretation ability of neural network. Experiment results show that this method is useful and applicable, and has better classification efficiency and classification availability than other pattern classification methods.