Texture is generally recognized as being fundamental to perception. A taxonomy of problems encountered within the context of texture analysis could be that of classification/discrimination, description, and segmentation. In this paper we suggest a novel artificial neural network (ANN) architecture for features extraction and texture recognition. There is evidence which suggests that the analysis of stimulus by visual system might involve a set of quasi-independent mechanisms called channels which could be conveniently characterized in the spatial frequency domain. In our model we use an FT feature space with angular and radial bins that characterize spatial domain filters to extract features. The extracted features are then used as input for the recognition stage. In order to evaluate the 2-D FT coefficients we use the Radon transform. The usage of the Radon transform simplifies the ANN model significantly. We suggest an electronic implementation of the ANN model for feature extraction, using a Connected Network Adaptive ProcessorS (CNAPS) chip designed by Adaptive Solutions Inc. We also develop software to simulate the ANN model with the Radon transform. We use a three stage back-propagation network as a classifier. We have used ten different texture patterns to test our ANN model.