A new unsupervised classification method using evolutionary strategies and fuzzy ART (adaptive resonance theory) neural networks is proposed in this paper. First, fuzzy ART neural networks is trained by original input samples under unsupervised way. Then evolutionary strategies is used to generate new training samples near the clusters boundaries of neural networks. Therefore the weights of fuzzy ART neural networks can be revised and refined by training those new generated samples under supervised way. The proposed method resolves the training problem for ART serial neural networks when there are only less training samples available. Consequently, it enhances the performance of ART serial neural networks and extends their application.