This paper describes the application of N-best lists to a spoken language translation system. Multiple hypotheses are generated both by the speech recognizer and by the statistical machine translator; they are finally re-ranked by optimally weighting recognition and translation scores, estimated in an integrated scheme. We provide experimental results for the Italian-to-English direction on the BTEC corpus, a collection of sentences in the touristic domain developed within the C-STAR project.