Recent progress in supervised image classification research, has demonstrated the potential usefulness of incorporating fuzziness in the training, allocation and testing stages of several classification techniques. In this paper a multiresolu­ tion neural network approach to supervised classification is presented, exploiting the inherent fuzziness of such tech­ niques in order to perform classification at different resolution levels and gain in computational complexity. In par­ ticular, multiresolution image analysis is carried out and hierarchical neural networks are used as an efficient archi­ tecture for classification of the derived multiresolution image representations. A new scheme is th