A novel neural network model of pre-attention processing in visual- search tasks is presented. Using displays of line orientations taken from Wolfe's experiments , we study the hypothesis that the distinction between parallel versus serial processes arises from the availability of global information in the internal representations of the visual scene. The model operates in two phases. First, the visual displays are compressed via principal-component-analysis. Second, the compressed data is processed by a target detector module in order to identify the existence of a target in the display. Our main finding is that targets in displays which were found experimentally to be processed in parallel can be detected by the system, while targets in experimentally-serial displays cannot. This fundamental difference is explained via variance analysis of the compressed representations, providing a numerical criterion distinguishing parallel from serial displays. Our model yields a mapping of response-time slopes that is similar to Duncan and Humphreys's "search surface" , providing an explicit formulation of their intuitive notion of feature similarity. It presents a neural realization of the processing that may underlie the classical metaphorical explanations of visual search.