Past decades have witnessed exciting work in the field of statistical machine translation (SMT). However, accurate evaluation of its potential in real-life contexts is still an open question. In this study, the authors investigate the behavior of an SMT engine faced with a corpus far different from the one it has been trained on. They show that terminological databases are obvious resources that should be used to boost the performance of a statistical engine. They propose and evaluate one way of integrating terminology into a SMT engine which yields a significant reduction in word error rate.