A VLSI implementation oriented off line handwritten digit classification system based on cellular neural network and two layer perceptron is presented in this paper.The cellular neural network is used to extract the connected component feature of normalized 20×20 handwritten digit image.These features are fed into the two layer perceptron after proper compression in time sharing mode.The two layer perceptron has a structure of 80×20×10 with local connection and it mainly consists of a weight programmable NPU array

switched current integrator and current comparators.This classification system has a smart structure and it can be directly mapped into current mode circuits whose technology is compatible with standard digital CMOS VLSI.