In this paper we discuss why special purpose chips are needed for useful implementations of connectionist neural networks in such applications as pattern recognition and classification. Three chip designs are described: a hybrid digital/analog programmable connection matrix, an analog connection matrix with adjustable connection strengths, and a digital pipe lined best-match chip. The common feature of the designs is the distribution of arithmetic processing power amongst the data storage to minimize data movement.