This paper describes a new technique for recognizing speech using visual speech information. The video data of the speaker?s mouth is represented using grayscale images named as motion history image (MHI). MHI is generated by applying accumulative image differencing on the frames of the video to implicitly represent the temporal information of the mouth movement. The MHIs are decomposed into wavelet sub images using Discrete Stationary Wavelet Transform (SWT). Three moment-based features (geometric moments, Zernike moments and Hu moments) are extracted from the SWT approximate sub images. Multilayer perceptron (MLP) type artificial neural network (ANN) with back propagation learning algorithm is used to classify the moments features. This paper evaluates and compares the image representation ability of the different moments. The initial experiments show that this method can classify English consonants with an error rate less than 5%.