Multiple approaches to grab comparable data from the Web have been developed up to date. Nevertheless, coming out with a high-quality comparable corpus of a specific topic is not straightforward. We present a model for the automatic extraction of comparable texts in multiple languages and on specific topics from Wikipedia. In order to prove the value of the model, we automatically extract parallel sentences from the comparable collections and use them to train statistical machine translation engines for specific domains. Our experiments on the English–Spanish pair in the domains of Computer Science, Science, and Sports show that our in-domain translator performs significantly better than a generic one when translating in-domain Wikipedia articles. Moreover, we show that these corpora can help when translating out-of-domain texts