In this paper we propose a new similarity index, which can be used to compare model-based clustering solutions. We define also an adjusted-for-chance version, although we advise that, whenever feasible, bootstrap replications should be preferred to chance-corrected similarity indices. We describe the properties of the proposed index and of its chance-corrected version. Finally, we present some applications on simulated and real data.