We investigate methods for identifying groups of patients with different prognosis. Our focus is on procedures that yield interpretable descriptions for groups of patients and corresponding regions of the predictor space. These strategies include tree-based methods and techniques which construct a single prognostic group through 'peeling' or refining of a larger group patients. The peeling methods are relatively new and are developed in detail. Simulations and examples for describing groups of patients with poor outcomes using data from a clinical trial for patients with multiple myeloma are presented.