Introduction to artificial neural networks, historical development and comparison with natural neural networks; the artificial neuron or processing element as a correlator; multi-layer networks and the role of a nonlinear activation function; multilayer perceptron with back-propagation training; gradient-descent versus higher-order training; radial basis function networks; matrix associative memory; Hopfield net as associative memory; competitive learning networks; Carpenter and Grossberg Adaptive Resonance Theory; Kohonen self-organising feature map; applications of neural networks in signal processing, image processing and machine vision, including associative memory, supervised and unsupervised classifiers and pattern recognition.