This paper describes the design and evaluation of an automatic speech recognition (ASR) system on the Naval Research Laboratory Speech In Noise (SPINE) speech corpus. This corpus represents a task which involves human-human interaction on a constrained problem solving scenario under six different simulated noisy environments. Acoustic and language modeling were performed using a dataset taken entirely from a subset of the acoustic environments. Speech recognition was performed on continuous conversations by detecting speech utterances, performing acoustic feature analysis and normalization, and adapting HMM models in multiple passes over each conversation-side. The ASR word accuracy (WAC) ranged from 77 percent in an office environment to 61 percent in conditions that include significant levels of background speech and noise. An overall average WAC of 69.0 percent was obtained across all noise conditions.