Over the last years, a number of methods have been proposed to automatically learn and optimize fuzzy rule bases from data. The obtained rule bases are usually robust and allow an interpretation even for data sets that contains imprecise or uncertain information. However, most of the proposed methods are still restricted to learn and/or optimize single layer feed-forward rule bases. The main disadvantages of this architecture are that the complexity of the rule base increases exponentially with the number of input and output variables and that the system is not able to store and reuse information of the past. Thus temporal dependencies have to be encoded in every data pattern. In this article we briefly discuss the advantages and disadvantages of hierarchical recurrent fuzzy systems that tackle these problems. Furthermore, we present a neuro-fuzzy model that has been designed to learn and optimize hierarchical recurrent fuzzy rule bases from data.