Current statistical machine translation systems usually build an initial word-to-word alignments before learning phrase translation pairs. This operation needs so many matching between di erent single words of both considered languages. We propose a new approach for phrase-based machine translation which does not need any word alignments, it is based on inter-lingual triggers determined by Multivariate Mutual Information. This algorithm segments sentences into phrases and nds their alignments simultaneously. The main objective is to build directly valid alignments between source and target phrases. Inspite of the youth of this method, experiments showed that the results are competitive but needs some more e orts in order to overcome the one of state-of-the-art methods.