We investigate the problem of passage retrieval for Question Answering (QA) systems. We adopt a machine learning approach and apply to QA a boosting algorithm initially proposed for ranking a set of objects by combining baseline ranking functions. The system operates in two steps. For a given question, it first retrieves passages using a conventional search engine and assigns each passage a series of scores. It then ranks the returned passages using a weighted feature combination. Weights express the feature importance for ranking and are learned to maximize the number of top ranked relevant passages over a training set. We empirically show that using questions from the TREC-11 question/answering track and the Aquaint collection, the proposed algorithm significantly increases both coverage and precision with respect to a conventional IR system.