Building an ASR system in adverse conditions is a challenging task. The performance of the ASR system is high in clean environments. However, the variabilities such as speaker effect, transmission effect, and the environmental conditions degrade the recognition performance of the system. One way to enhance the robustness of ASR system is to use multiple sources of information about speech. In this work, two sources of additional information on speech are used to build a multimodal ASR system. A throat microphone speech and visual lip reading which is less susceptible to noise acts as alternate sources of information. Mel-frequency cepstral features are extracted from the throat signal and modeled by HMM. Pixel-based transformation methods (DCT and DWT) are used to extract the features from the viseme of the video data and modeled by HMM. Throat and visual features are combined at the feature level. The proposed system has improved recognition accuracy compared to unimodals. The digit database for the English language is used for the study. The experiments are carried out for both unimodal systems and the combined systems. The combined feature of normal and throat microphone gives 86.5% recognition accuracy. Visual speech features with the normal microphone combination produce 84% accuracy. The proposed work (combines normal, throat, and visual features) shows 94% recognition accuracy which is better compared to unimodal and bimodoal ASR systems. © Springer Nature Singapore Pte Ltd. 2019.