Example-Based Machine Translation can be applied to languages whose resources like dictionaries, reliable syntactic analyzers are hardly available because it can learn from new translation examples. However, difficulties still remain in translation of sentences which are not fully covered by the matching sentence. To solve that problem, we present in this paper a translation method which recursively divides a sentence and translates each part separately. In addition, we evaluate an analogy-based word-level alignment method which predicts word correspondences between source and translation sentences of new translation examples. The translation method was implemented in a French-Japanese machine translation system and spoken language text were used as examples. Promising translation results were earned and the effectiveness of the alignment method in the translation was confirmed.