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This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training-set parallelism. Both theoretical analysis and experimental results for several bench mark problems are described. Guidelines for efficient parallel implementation are also highlighted. This book is suitable for graduate students, research scientists and practising engineers in the fields of artificial neural networks and parallel computing.