In this communication, we present a method for noise-robust multimicrophone automatic speech recognition (ASR). It is assumed that the speech source to be recognized is recorded with several microphones in a noisy acoustic environment. The proposed method estimates the short-term subband energies (as they are needed for computing the ASR front-end) of the clean speech source from the ones of the microphone noisy signals. The estimation procedure is based on the concept of Independent Component Analysis (ICA) and it is driven by the acoustic model used by the ASR decoder. The method is shown to be highly robust for a connected digit recognition task in high noise conditions, improving word error rates by more than 50% relatively to the performance of the baseline single-microphone ASR system.