The paper proposes and evaluates a binary network architecture and complementary training algorithm designed for pattern classification, with applications in a variety of engineering problems. The advantages of the system are that it can always converge on zero error for a set of unambiguous training patterns (if required), converges rapidly, and circumvents the issue of how many hidden neurons to incorporate in a network. The main benefit of the proposed system over Hamming networks, its main counterpart, is that it can group patterns into different classes along any boundary. The system is shown to outperform 1) the Hamming network for a character recognition problem where the images are subject to both position change and noise; and 2) a radial-Gaussian network in a truck-type classification problem. A variant of the system, where hidden neuron thresholds are set to zero, is shown to further improve performance if a comprehensive set of noise-free input patterns are available for training.