Abstract. This paper presents a lip-reading technique to identify the unspoken phones using support vector machines. The proposed system is based on temporal integration of the video data to generate spatiotemporal templates (STT). 64 Zernike moments (ZM) are extracted from each STT. This work proposes a novel feature selection algorithm to reduce the dimensionality of the 64 ZM to 12 features. The proposed technique uses the shape of probability curve as a goodness measure for optimal feature selection. The feature vectors are classified using nonlinear support vector machines.Such a system could be invaluable when it is important to communicate without making a sound, such as giving passwords when in public spaces.