This paper describes an audio visual speech recognition (AVSR) system based on articulatory features (AF). It implements a tandem approach where artificial neural networks (ANN), in particular multi-layer perceptrons (MLP), are used as posterior probability estimators for transforming raw input data into the more abstract articulatory features. Such an approach is particularly well suited if relatively few training data are available, a situation which is typical for AVSR. In addition, the MLP feature extraction results and some analysis in terms of recognition accuracy and confusions are presented. Our AF-based AVSR system has been trained on the audio-visual speech corpus VIDTIMIT, which contains conversational speech based on a medium size vocabulary including more than 1200 words.