Selective visual attention serializes the processing of stimulus data to make efficient use of limited processing resources in the human visual system. This paper describes a connectionist network that exhibits a variety of attentional phenomena reported by Treisman, Wolford, Duncan, and others. As demonstrated in several simulations, a hierarchical, multiscale network that uses feature arrays with strong lateral inhibitory connections provides responses in agreement with a number of prominent behaviors associated with visual attention. The overall network design is consistent with a range of data reported in the psychological literature, and with neurophysiol-ogical characteristics of primate vision.