For helicopters, anticipating failures is crucial. To this end, the analysis of flight data allows to develop predictive maintenance approaches , for which Airbus Helicopters (AH) has proposed several solutions , some based on machine learning using predictive models. One recurrent problem in this setting is the contextualization of the data, that is to identify the data better fitting the phenomenon being mod-eled. Indeed, helicopters are complex systems going through different flight phases. Experts therefore have to identify the adequate ones, in which the selected flight parameters are stable and consistent with the studied problem. In this paper, we propose a generic solution to con-textualize classification data, and present an experimental study on AH flight data: the results are encouraging and allow to keep domain experts involved the process.