We present a new data analysis framework, Multi-Objective Markov Decision Processes for Decision Support, for developing sequential decision support systems. The framework extends the MultiObjective Markov Decision Process with the ability to provide support that is tailored to different decisionmakers with different preferences about which objectives are most important to them. We present an extension of fitted-Q iteration for multiple objectives that can compute recommended actions in this context; in doing so we identify and address several conceptual and computational challenges. Finally, we demonstrate how our model could be applied to provide decision support for choosing treatments for schizophrenia using data from the Clinical Antipsychotic Trials of Intervention Effectiveness.