This paper describes an Example-Based Machine Translation prototype and presents an evaluation of the impact of using a domain specific vocabulary on its performance. This prototype is based on a hybrid approach which needs only monolingual texts in the target language and consists to combine translation candidates returned by a cross-language search engine with translation hypotheses provided by a finite-state transducer. The results of this combination are evaluated against a statistical language model of the target language in order to obtain the n-best translations. To measure the performance of this hybrid approach, we achieved several experiments using corpora on two domains from the European Parliament proceedings (Europarl) and the European Medicines Agency documents (Emea). The obtained results show that the proposed approach out performs the state-of-the-art Statistical Machine Translation system Moses when texts to translate are related to the specialized domain.