In industrial polymerization processes, several grades of polymer are frequently produced in the same plant by changing the operating conditions. Transitions between the different grades are rather slow and result in the production of a considerable amount of off-specification polymer. Grade transition improvement is viewed here as a dynamic optimization problem, for which numerous approaches exist. Open-loop implementation of the input profiles obtained from numerical optimization of a nominal process model is often insufficient, due to the presence of uncertainty in the form of model mismatch and process disturbances. A novel measurement-based approach that consists of tracking the necessary conditions of optimality (NCO tracking) using a solution model and measurements is considered. The solution model consists of state-event-triggered controllers sequenced according to the structure of the nominal optimal solution computed offline. The solution model is generated by expressing the input profiles in terms of arcs and switching times, which are then related to the various parts of the NCO, that is, to the active constraints and sensitivities. These arcs and switching times are then adapted online using appropriate measurements. The application of NCO tracking to an industrial polymerization process for implementing optimal grade transitions is investigated in simulation. The grade transition problem. is fairly complex due to a large-scale process model, many degrees of freedom, as well as path and endpoint constraints. A solution model is generated from the nominal optimal solution, and a control superstructure is considered to handle the possible activation of nominally-inactive constraints. Simple PI-type controllers are used to implement the solution model. For different uncertainty scenarios, simulation of the NCO-tracking approach shows that considerable reduction in transition time is possible, while still guaranteeing feasible operation.