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An efficient method for selection of features suitable for classification of textured images is presented. The spatial interaction of gray levels in a local neighbourhood N is modeled by stochastic random field models. The estimates of the model parameters are taken as textural features denoted by fN. Selection of an N that would yield powerful features is done through visual examination of images synthesized using fN. Experimental studies involving nine different types of natural textures yield 97% classification accuracy.