A new algorithm for synthesizing binary threshold neural networks (BTNNs) is proposed. A binary (Boolean) input-output mapping that can be represented by minimal sum-of-product (MSP) terms is initially obtained from training data. The BTNN is then synthesized based on an MSP term grouping method. As a result, a fast and optimal realization of a BTNN can be obtained. Examples of both feedforward and recurrent BTNN synthesis used in a parallel processing architecture are given and compared with other existing methods.