Time is involved in almost every scienti c eld one can think on. Observations of a phenomena are collected with the aim of study or explain its behavior. This collections lead to organized data called time series. Data mining community has spent a reasonable amount of time studying time series, in order to extract all meaningful knowledge from them. Humans are generally good comparing time series, but still, our capabilities are not scalable and we need to design algorithms and techniques that allow us to deal with high dimensional data and other problems. In this work we will focus in a speci c problem, extracting valid features of unlabeled time series obtained from aircraft sensors. These must serve as a summary of a ight and they also must include relevant details that serve to characterize it. This information will be used to feed an algorithm which can learn to classify ights in groups, reducing the number of necessary labeled data to obtain the desired accuracy using an active learning approach.