A new approach to interconnecting multi-layer feedforward neural networks for tackling the problems of texture classification is proposed. The resulting classification system classifies textures via two stages; one to compress original co-occurrence feature patterns of high dimensionality to lower dimensional principal feature patterns, and the other to perform actual classification of textures using the principal features. Each stage is efficiently implemented by a trained multi-layer feedforward neural network. Such a cascaded use of neural networks significantly reduces the computational complexity that is otherwise encountered in classifying large-scale texture images. Two practical applications of the system are provided, showing the direct applicability of the approach for real problem-solving.