This paper proposes a method for example-based machine translation that combines syntactic transfer with statistical models. The method uses transfer rules to construct the target language syntactic tree structure from the source language. It then uses a statistical generation module to select the best word sequence based on language and translation models. The method is evaluated on a travel domain corpus, with the combined approach outperforming a baseline of example-based transfer alone in terms of BLEU, NIST and human evaluation.