A new fuzzy ARTMAP neural network with adaptive cluster vigilance parameter is proposed. Each cluster (hyper rectangle) has its own vigilance parameter which is adaptively adjusted during the training procedure. And a new learning law that defines the establishments and expansions for either non overlapped hyper rectangles or non overlapped nested hyper rectangles is proposed. The proposed neural network can obtain high prediction rate

which has resolved not only the problem of memory stability and plasticity but also the problem of non convex input patterns

both of which exist in traditional fuzzy ARTMAP neural networks. The simulations of applying the new net to palm prints recognition demonstrate its good performance.