Prosopagnosia is one of the many forms of visual associative agnosia, in which familiar faces lose their distinctive association. In the case of prosopagnosia, the ability to recognize familiar faces is lost, due to lesions in the medial occipitotemporal region. In "associative" prosopagnosia, the perceptual system seems adequate to allow for recognition, yet recognition cannot take place. Our hypothesis is that a possible cause of associative prosopagnosia might be the occurrence of Dynamic attractors in the brain's auto-associative circuits. We present a biologically plausible model composed of two stages: Pre-processing and face recognition. In the first stage, the face image is passed through Gabor filters which model the kind of visual processing carried out by the simple and complex cells of the primary visual cortex of higher mammals and the resulting features are fed into a Pseudo-inverse associative neural network for the recognition task. Next, we damage the network by reducing self-connections below a certain threshold in order to create dynamic attractors and hence hinder the networks ability to recognize familiar faces (faces already learned). Results obtained from simulations show that the resulting network responses are very similar to those of associative prosopagnosic patients. We conclude that the problems concerning associative prosopagnosia may partly be explained through the concepts of dynamic attractors. Although there is no known cure for prosopagnosia, we believe that the focus of any treatment should be to help the individual with prosopagnosia develop compensatory strategies for remembering faces. Adults with prosopagnosia as a result of stroke or brain trauma can be retrained to use other clues to identify faces. And a cure for children born with prosopagnosia might eventually rely on reinforcement techniques that reward them for paying attention to faces during early childhood. Reinforcement learning from examples of patterns to be classified using habituation and association would create lower dimensional local basins in the brain, which would form a global attractor landscape with one basin for each face. These local basins would eventually constitute dynamical memories that solve difficult problems in classifying and recognizing faces.