Optimization in the process industry has received a lot of attention in recent years because, in the face of growing competition, it represents a natural choice for reducing production costs, improving product quality, and meeting safety requirements and environmental regulations. Traditionally, the optimal operating conditions are determined based on a model of the process. However, the resulting process operation can be highly sensitive to uncertainty such as model mismatch and process disturbances. This generally gives rise to suboptimal process operation or, worse, infeasible operation, which of course is not tolerable in most industrial applications. Over the last decade, the {\it Laboratoire d'Automatique} of the EPFL has developed a promising approach that converts a dynamic optimization problem with both path and terminal constraints into a feedback control problem. In this approach, near-optimal process operation is enforced by tracking appropriate references, namely the {\it necessary conditions of optimality} (NCO). The NCO-tracking framework is thus much appealing in its on-line simplicity and its potential to be robust towards uncertainty and it has gained substantial international recognition. In this presentation, we give an overview of the current state-of-the-art in NCO tracking. Special emphasis is placed on the industrially relevant features of NCO tracking. We conclude the talk by presenting a number of future research directions in this area.