Although advances in close-talk speech recognition have resulted in relatively low error rates, the recognition performance in far-field environments is still limited due to low signal-to-noise ratio, reverberation, and overlapped speech from simultaneous speakers which is especially more difficult. To solve these problems, beamforming and speech separation networks were previously proposed. However, they tend to suffer from leakage of interfering speech or limited generalizability. In this work, we propose a simple yet effective method for multi-channel far-field overlapped speech recognition. In the proposed system, three different features are formed for each target speaker, namely, spectral, spatial, and angle features. Then a neural network is trained using all features with a target of the clean speech of the required speaker. An iterative update procedure is proposed in which the mask-based beamforming and mask estimation are performed alternatively. The proposed system were evaluated with real recorded meetings with different levels of overlapping ratios. The results show that the proposed system achieves more than 24% relative word error rate (WER) reduction than fixed beamforming with oracle selection. Moreover, as overlap ratio rises from 20% to 70+%, only 3.8% WER increase is observed for the proposed system.