Considerable research effort is being devoted to the development of image-enhancement algorithms, which improve the quality of displayed digital pictures. Reliable methods for measuring perceived image quality are needed to evaluate the performances of those algorithms, and such measurements require a univariant (i.e., no-reference) approach. The system presented in this paper applies concepts derived from computational intelligence, and supports an objective quality-assessment method based on a circular back-propagation (CBP) neural model. The network is trained to predict quality ratings, as scored by human assessors, from numerical features that characterize images. As such, the method aims at reproducing perceived image quality, rather than defining a comprehensive model of the human visual system. The connectionist approach allows one to decouple the task of feature selection from the consequent mapping of features into an objective quality score. Experimental results on the perceptual effects of a family of contrast-enhancement algorithms confirm the method effectiveness, as the system renders quite accurately the image quality perceived by human assessors. © 2005 Elsevier B.V. All rights reserved.