Pattern recognition: supervised and unsupervised learning in pattern recognition; nonparametric decision theoretic classification; nonparametric (distribution-free) training of discriminant functions; statistical discriminant functions; clusteringanalysis and unsupervised learning; dimensionality reduction and feature selection. Neural networks for pattern recognition: multilayer perception; radial basis function networks; hamming net and Kohonen self-organizing feature map; the Hopfield model.Data preprocessing for pictorial pattern recognition: preprocessing in the spatial domain; pictorial data preposessing and shape analysis; transforms and image processing in the transform doamin; wavelets and wavelet transforms. Applications: exemplaryapplications. Practical concerns of image processing and pattern recognition: computer system architectures for image processing and pattern recognition. Appendices: digital images; image model and discrete mathematics; digital image fundamentals; matrixmanipulation; Eigenvectors and Eigenvalves of an operator; notation.