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The Visual Speech Recognition (VSR) system performance is highly influenced by the selection of visual features. These features are categorized into static and dynamic features. This work proposes to exploit both lip shape (static-geometric features) as well as the temporal sequence of lip movements (dynamic-motion features) to build a combined VSR system with fusion both at feature level and model level. The digit dataset for VSR system is evaluated on the benchmark (using Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Zernike Moments (ZM)) systems. First, the Motion History Image (MHI) is calculated from all visemes from which wavelet and Zernike coefficients are extracted and modeled using a simple GMM L-R HMM. This proposed method shows a significant improvement in performance of 85% for MHI-DWT based features, 74% for MHI-DCT and 80% for MHI-ZM features …