Texture plays an increasingly important role in computer vision. It has found wide application in remote sensing, medical diagnosis, quality control, food inspection and so forth. This thesis investigates the problem of classifying texture in digital images, following the convention of splitting the problem into feature extraction and classification. Texture feature descriptions considered in this thesis include Liu's features, features from the Fourier transform using geometrical regions, the Statistical Gray-Level Dependency Matrix, and the Statistical Feature Matrix. Classification techniques that are considered in this thesis include the K-Nearest Neighbour Rule and the Error Back-Propagation method. Novel techniques developed during the author's Ph.D study include (1) a Generating Shrinking Algorithm that builds a three-layer feed-forward network to classify arbitrary patterns with guaranteed convergence and known generalisation behaviour, (2) a set of Statistical Geometrical Features for texture analysis based on the statistics of the geometrical properties of connected regions in a sequence of binary images obtained from a texture image, (3) a neural implementation of the K-Nearest Neighbour Rule that can complete a classification task within 2K clock cycles. Experimental evaluation using the entire Brodatz texture database shows that (1) the Statistical Geometrical Features give the best performance for all the considered classifiers, (2) the Generating Shrinking Algorithm offers better performance over the Error Back-Propagation method and the K-Nearest Neighbour Rule's performance is comparable to that of the Generating Shrinking Algorithm, (3) the combination of the Statistical Geometrical Features with the Generating-Shrinking Algorithm constitutes one of the best texture classification systems considered.