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This work applies neural-network technologies to the quality assessment of digital pictures processed by image-enhancement algorithms. The objective model uses a circular back-propagation (CPB) neural network to mimic human perception: the feed-forward structure maps input 'feature' vectors characterizing images into the associated quality ratings, obtained from human voters. "Objective" feature vectors describe images by measuring global statistical properties, which are worked out on a block-by-block basis. CPB networks can handle multidimensional data with non-linear relationships; at the same time, the neural model allows one to decouple the feature-selection task from the mapping-function set-up. Experimental results confirm the approach effectiveness, as the system provides a satisfactory approximation of the results of tests involving human viewers.