So far, many effective hypothesis alignment metrics have been proposed and applied to the system combination, such as TER, HMM, ITER and IHMM. In addition, the Minimum Bayes-risk (MBR) decoding and the confusion network (CN) have become the state-of-the-art techniques in system combination. In this paper, we present a three-pass system combination strategy that can combine hypothesis alignment results derived from different alignment metrics to generate a better translation. Firstly the different alignment metrics are carried out to align the backbone and hypotheses, and the individual CN is built corresponding to each alignment results; then we construct a super network by merging the multiple metric-based CN and generate a consensus output. Finally a modified consensus network MBR (ConMBR) approach is employed to search a best translation. Our proposed strategy outperforms the best single CN as well as the best single system in our experiments on NIST Chinese-to-English test set.