A multitask neural network is proposed as a plausible visual information processor for performing a variety of real-time operations associated with the early stages of vision. The computational role performed by the processor, named the positive-negative (PN) neural processor, emulates the spatiotemporal information processing capabilities of certain neural activity fields found along the human visual pathway. The state-space model of this visual information processor corresponds to a bilayered two-dimensional array of densely interconnected nonlinear processing elements (PE's). An individual PE represents the neural activity exhibited by a spatially localized subpopulation of excitatory or inhibitory nerve cells. Each PE may receive inputs from an external signal space as well as from itself and the neighboring PE's within the network. The information embedded in the external input data which originates from a video camera or another processor is extracted by the feedforward subnet. The feedback subnet of the PN neural processor generates a variety of transient and steady-state activities. Their various computational roles are applicable to gray level, edge, texture, or color information processing. Computer simulations involving gray level image processing are used to illustrate the versatility of the PN neural processor architecture for machine vision system design.