Automatic Speech Recognition systems commonly separate speech enhancement, including localization, beamforming and postfiltering, from acoustic modeling. In this talk, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5% compared to a traditional beamforming-based multichannel ASR system and more than 10% compared to a single channel model.