Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an expert in the category of information it has been given to analyze. Neural network is applied in LIP READING, one of the easiest ways to recognize the speech. It is one of the latest techniques widely preferred for speech recognition. We describe a lip reading system that uses both, shape information from the lip contours and intensity information from the mouth area. Shape information is obtained by tracking and parameterising the inner and outer lip boundary in an image sequence. Intensity information is extracted from a grey level model, based on principal component analysis. In comparison to otherapproaches, the intensity area deforms with the shape model to ensure that similar object features are represented after non-rigid deformation of the lips. We describe speaker independent recognition experiments based on these features.