Abstract. We describe a new approach for synthetically combining the output of several different Machine Translation (MT) engines operating on the same input. The goal is to produce a synthetic combination that surpasses all of the original systems in translation quality. Our approach uses the individual MT engines as “black boxes ” and does not require any explicit cooperation from the original MT systems. An explicit word matcher is first used in order to identify the words that are common between the MT engine outputs. A decoding algorithm then uses this information, in conjunction with confidence estimates for the various engines and a trigram language model in order to score and rank a collection of sentence hypotheses that are synthetic combinations of words from the various original engines. The highest scoring sentence hypothesis is selected as the final output of our system. Experiments conducted using three Chinese-to-English online translation systems demonstrate that our multi-engine combination system provides an improvement of about 6 % over the best original system, and is about equal in translation quality to an “oracle ” capable of selecting the best of the original systems on a sentence-by-sentence basis. A second oracle experiment shows that our new approach produces synthetic combination sentence hypotheses that are far superior to the hypotheses currently selected by the system, but our current scoring is not yet capable of adequately identifying the best hypothesis. 1.