Existing Audio-Visual Speech Recognition (AVSR) systems visually focus intensely on a small region of the face, centred on the immediate mouth area. This is poor design for a variety reasons in real world situations because any occlusion to this small area renders all visual advantage null and void. This is poorby design because it is well known that humans use the complete face to speechread. We demonstrate a new application of a novel visual algorithm, the Multi-Channel Gradient Model, the deploys information from the complete face to perform AVSR. Our MCGM model performs near to the performance of Discrete Cosine Transforms in the case where a small region of interest around the lips, but in the case of an occluded face we can achieve results that match nearly 70% of the performance that DCTs can achieve on the DCT best case, lips centeric approach.