Audio-visual automatic speech recognition systems use visual information to enhance ASR systems in clean and noisy environments. This paper investigates a number of different visual feature extraction methods. It was observed that when performing visual speech recognition the visual feature vector requires a base level of detail for improved recognition. Geometric feature extraction provides lower recognition than pixel based methods due to the loss of characteristic speech information such as protrusion etc. Downsampling of images reduces visual recognition scores due to the loss of detail in the images. Also, the role of dynamic features was investigated for improved recognition. It was observed that static features alone outperform a combination of both static and dynamic features when restricting the dimension of the feature vector e.g. 50. This illustrates that the need for a certain level of detail in visual speech recognition is a higher priority than dynamic information. Once this base level of detail is attained the dynamic features should then be able to improve the recognition rate.