While it is generally agreed that Word Sense Disambiguation (WSD) is an application-dependent task, the great majority of systems pursue application-independent approaches. We propose a strategy to support WSD for Machine Translation which is designed specifically for this application. It relies on the analysis of co-occurrences in the context that refer to words which have already been translated. Experiments on the English-Portuguese translation of 10 verbs using just this knowledge yielded an accuracy of 51%, which outperforms the baseline using the most frequent translation (37%). A less strict evaluation criterion considering the 10 best ranked translations proved the potential for this approach to be used as extra knowledge source for WSD: the correct translation was among the top 10 results in 92% of the cases.