New results, which have been achieved by using neural network architectures for two-dimensional image classification based on the goal-seeking neuron (GSN), are presented. A number of important practical issues concerning mapping topologies and the parallel implementation of GSN-based architectures are also investigated, together with a proposal for the development of a related neurally based feature extractor to be used as a front-end processor in a fully integrated Boolean network architecture.<<ETX>>