Clustering high-dimensional data is challenging. Classic metrics fail in identifying real similarities between objects. Moreover, the huge number of features makes the cluster interpretation hard. To tackle these problems, several co-clustering approaches have been proposed which try to compute a partition of objects and a partition of features simultaneously. Unfortunately, these approaches identify only a predefined number of flat co-clusters. Instead, it is useful if the clusters are arranged in a hierarchical fashion because the hierarchy provides insides on the clusters.In this paper we propose a novel hierarchical co-clustering, which builds two coupled hierarchies, one on the objects and one on features thus providing insights on both them. Our approach does not require a pre-specified number of clusters, and produces compact hierarchies because it makes ro-ary splits, where ro is automatically determined. We validate our approach on several high-dimensional datasets with state of the art competitors.