This paper proposes a new multi-modal speech recognition method using optical-flow analysis, evaluating its robustness to acoustic and visual noises. Optical flow is defined as the distribution of apparent velocities in the movement of brightness patterns in an image. Since the optical flow is computed without extracting speaker's lip contours and location, robust visual features can be obtained for lip movements. Our method calculates a visual feature set in each frame consisting of maximum and minimum values of integral of the optical flow. This feature set has not only silence information but also open/close status of the speaker's mouth. The visual feature set is combined with an acoustic feature set in the framework of HMM-based recognition. Triphone HMMs are trained using the combined parameter set extracted from clean speech data. Two multi-modal speech recognition experiments have been carried out. First, acoustic white noise was added to speech wave forms, and a speech recognition experiment was conducted using audio-visual data from 11 male speakers uttering connected Japanese digits. The following improvements of relative reduction of digit error rate over the audio-only recognition scheme were achieved, when the visual information was incorporated into silence HMM: 32% at SNR=10dB and 47% at SNR=15dB. Second, a real-world data distorted both acoustically and visually was recorded in a driving car from six male speakers and recognized. We achieved approximately 17% and 11% relative error reduction compared with audio-only results on batch and incremental MLLR-based adaptation, respectively.