Publisher Summary The integrated process supervision (IPS) architecture is a refinement of the expert control scheme that constitutes a generic framework for building online supervision and control systems. It allows for meaningful combination of both traditional and artificial-intelligence-based control techniques. It provides a high degree of reusability as well as portability. The IPS is a structured approach based on two prominent concepts: the rigorous classification of process behaviors and control tasks, and the use of standard performance indices to identify behaviors and schedule control activities accordingly. The IPS system realizes the supervised evolution of process control through a robust fuzzy-neural rule-based scheduling of different control regimes: primary control, adaptive control, and model-based fault diagnosis and correction. The IPS system has been validated in simulation as well as successfully implemented in real time on an industrial heat exchanger. This chapter introduces the problem of process control, along with existing solutions from both classical control theory and artificial intelligence. This introduction leads to the definition of knowledge-based supervision and a brief survey of related expert control approaches. It highlights the basic concepts of the integrated process supervision framework. It presents the architecture and various components of the IPS, realization of multiple instances of IPS, along with experimental results. It focuses on the principal component of the IPS, namely, the rule-based process supervisor—a real-time microcontroller-based realization of the IPS that integrates both conventional and neural control algorithms within the system. This chapter closes with an account on current and future issues.