This paper describes the MOUNTAIN language generation system, a fully-automatic, data-driven approach to natural language generation aimed at spoken dialog applications. MOUNTAIN uses statistical machine translation techniques and natural corpora to generate human-like language from a structured internal language, such as a representation of the dialog state. We briefly describe the training process for the MOUNTAIN approach, and show results of automatic evaluation in a standard language generation domain: the METEO weather forecasting corpus. Further, we compare output from the MOUNTAIN system to several other NLG systems in the same domain, using both automatic and human-based evaluation metrics; our results show our approach is comparable in quality to other advanced approaches. Finally, we discuss potential extensions, improvements, and other planned tests.