This paper describes the University of Washington's submission to the IWSLT 2006 evaluation campaign. We present a multi-pass statistical phrase-based machine translation system for the Italian-English open-data track. The focus of our work was on the use of heterogeneous data sources for training translation and language models, the use of several novel rescoring features in the second pass, and exploiting N-best information for translation in the ASR-output condition. Results show mixed bene&# 2;ts of adding out-of-domain data and using N-best information and demonstrate improvements for some of the novel rescoring features.