The rapid progress in digital music distribution has lead to the creation of large collections of music. There is a need for content-based music classification methods to organize these collections automatically using a given genre taxonomy. To provide a versatile description of the music content, several kinds of features like rhythm, pitch or timbre characteristics are commonly used. Taking the highly dynamic nature of music into account, each of these features should be calculated up to several hundreds of times per second. Thus, a piece of music is represented by a complex object given by several large sets of feature vectors. In this paper, we propose a novel approach for the hierarchical classification of music pieces into a genre taxonomy. Our approach is able to handle multiple characteristics of music content and achieves a high classification accuracy efficiently, as shown in our experiments performed on a real world data set.