The fact that machine translation output does not reach the level of good human translations is well known. However, there has been surprisingly little attention paid by the machine translation community to how humans achieve such results. In this paper, I suggest several ways to improve machine translation, based on the best practices of human translators, as described in Nida's (1964) Toward a Science of Translating. I call this approach multi-pass machine translation (MPMT), as it crucially relies on processing the text more than once. It is similar to the opportunistic bricoleur approach of Gdaniec (1999) in that it sets out to use the means at hand, adding to or changing them as necessary. As Sch utz (2001) points out, much of the research in the past decade has concentrated on the important but non-core issues of integrating MT into DTP formats and HTML. In this paper I concentrate on improving the MT engine itself. The resulting approach integrates much recent research into a single system.