The indifference-zone (IZ) formulation of ranking and selection (R&S) is the foundation of many procedures that have been useful for choosing the best among a finite number of simulated alternatives. Of course, simulation models are imperfect representations of reality, which means that a simulation-based decision, such as choosing the best alternative, is subject to model risk. In this paper we explore the impact of model risk due to input uncertainty on IZ R&S. Input uncertainty is the result of having estimated (fit) the simulation input models to observed real-world data. We find that input uncertainty may force the user to revise, or even abandon, their objectives when employing a R and S procedure, or it may have very little effect on selecting the best system even when the marginal input uncertainty is substantial.