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Texture can be considered as a repeating pattern of local variation of pixel intensities. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. One of the difficulties in texture classification was the lack of tools that characterize textures. Classification of textures has received attention during last few decades. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. Then DCT is applied on the gray level images to obtain DCT coefficient. These DCT coefficient are use to train the neural network. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A performance comparison was made among the soft computing models for the texture classification problem. It is observed that the proposed neuro-fuzzy model performed better than the neural network.