The MIT-LL/AFRL MT system is a statistical phrase-based translation system that implements many modern SMT training and decoding techniques. Our system was designed with the long-term goal of dealing with corrupted ASR input and limited amounts of training data for speech-to-speech MT applications. This paper will discuss the architecture of the MIT-LL/AFRL MT system, improvements over our 2005 system, and experiments with manual and ASR transcription data that were run as part of the IWSLT-2006 evaluation campaign.