This presentation discusses real-time optimization (RTO) strategies for improving process performance in the presence of uncertainty in the form of plant-model mismatch, drifts and disturbances. RTO typically uses a plant model to compute optimal inputs. In the presence of uncertainty, selected model parameters can be estimated and the updated model used for optimization. Although very intuitive, this two-step approach suffers from the fact that the model is almost invariably inadequate, which prevents from reaching the plant optimum. Other approaches have been developed in the last two decades to overcome this difficulty. Recently, a generic formalization of these ad hoc fixes has been proposed under the label modifier adaptation. The basic idea is to leave the model parameters unchanged but to use the plant measurements to “appropriately” modify the optimization problem. The modifier-adaptation approach will be presented and compared to the two-step approach, in particular with regard to model adequacy. We will then go beyond this comparison and discuss different ways of using plant measurements for process improvement in the presence of uncertainty. There are many questions to be addressed: (i) what can be done off-line prior to process operation, and what should be performed in real time, (ii) how much of the optimization effort is model-based and how much is data-driven, (iii) what to measure, what to adapt, how to adapt? We will then see that there exists another class of measurement-based optimization approaches that implements direct input adaptation. This class of methods includes NCO tracking, extremum-seeking control and self-optimizing control. A case study will illustrate the applicability of the various approaches.