Organizing legislative texts into a hierarchy of legal topics enhances the access to legislation. Manually placing every part of new legislative texts in the correct place of the hierarchy, however, is expensive and slow, and therefore naturally calls for automation. In this paper, we assess the ability of machine learning methods to develop a model that automatically classifies legislative texts in a legal topic hierarchy. It is investigated whether such methods can generalize across different codes. In the classification process, the specific properties of legislative documents are exploited. Both the hierarchical structure of legal codes and references within the legal document collection are taken into account. We argue for a closer cooperation between legal and machine learning experts as the main direction of future work.