The construction of decision trees is an efficient tool for inductive learning, and fuzzy decision trees are particularly interesting because they enable the user to take into account imprecise descriptions of the cases, or heterogeneous values (symbolic, numerical, or fuzzy). However, since the method to construct a fuzzy decision tree is not unique, in this paper, a comparative study is presented to point out differences between three methods. This study focus on differences between methods when ranking attributes during the construction of a fuzzy decision tree. The aim is to enable the reader to understand what kind of fuzzy decision tree is obtained by each method.