In this poster, we develop a novel method, called HCC, for hierarchical co-clustering. HCC brings together two interrelated but distinct themes from clustering: hierarchical clustering and co-clustering. The goal of the former theme is to organize clusters into a hierarchy that facilitates browsing and navigation, while the goal of the latter theme is to cluster different types of data simultaneously by making use of the relationship information. Our initial empirical results are promising and they demonstrate that simultaneously attempting both these goals in a single model leads to improvements over models that focus on a single goal.