In this paper, a study is presented to explore ensembles of fuzzy decision trees. First of all, a quick recall of the state of the art related to ensembles of (fuzzy) decision trees in Machine Learning is presented. Afterwards, a new approach to construct a forest of fuzzy decision trees is proposed. Two experiments are described, one with forests of fuzzy decision trees, and the other with bagging of fuzzy decision trees. The results highlight the interest of using fuzzy set theory in this kind of approaches.