This paper presents an automatic speech recognition (ASR) system for the 5th CHiME Speech Separation and Recognition Challenge for transcribing continuous conversations recorded in everyday environments with distributed microphone arrays. The main contribution of the proposed system is the investigation of an effective real-time channel selection scheme to pick up reliable microphones/array for target speakers. It is shown that the proposed channel selection method produces better ASR performance than the reference channel provided by the baseline system, as well as comparable results to a reference-required selection approach based on speech intelligibility test as the oracle case. Instead of including all available data for training data augmentation, channel selection can be also applied to the training data selection to minimize the training-test mismatch. Further, complementary knowledge can be obtained when the best two channels are selected specially in periods of overlapping speech. The final ASR system, which additionally incorporates improved acoustic modeling and system combination, achieves absolute word error rate reductions of 9.2% and 7.0% with development and evaluation test set, respectively, compared to the baseline in the context of multiple-array track.