Nowadays, the annotation of videos with high-level semantic concepts or features is a great challenge. In this paper, this problem is tackled by learning, by means of Fuzzy Decision Trees (FDT), automatic rules based on a limited set of examples. Rules intended, in an exploitation step, to reduce the need of human usage in the process of indexation. However, when addressing large, unbalanced, multiclass example sets, a single classifier - such as the FDT - is insufficient. Therefore we introduce the use of forests of fuzzy decision trees (FFDT) and we highlight: (a) its effectiveness on a high level feature detection task, compared to other competitive systems and (b) the effect on
performance from the number of classifiers point of view. Moreover, since the resulting indexes are, by their nature, to be used in a retrieval application, we discuss the results under the lights of a ranking (vs. a classification) context.