This paper presents an empirical comparison of process control algorithms, with particular emphasis on classical Proportional–Integral–Derivative (PID) control, Model Predictive Control (MPC), and neural network-based methods in the context of complex industrial plants. Since industrial sectors frequently demand improvements in operational efficiency, product quality, and safety, it is plausible that robust and adaptable control systems are increasingly necessary to accommodate varying capacity requirements. The investigation made use of a real-world dataset alongside a simulated refinery dataset from the Tennessee Eastman (TE) process to benchmark the selected control strategies under diverse disturbances and process conditions. Evidence suggests that neural network-based controllers frequently achieved superior disturbance rejection and more accurate setpoint tracking than conventional PID or MPC designs. The evaluation was based on established quantitative measures, including the Integral of Squared Error (ISE) and the Integral of Absolute Error (IAE). The data seems to indicate that the enhanced performance of neural network controllers may be associated with their ability to model nonlinear process dynamics more effectively. However, challenges relating to their implementation in safety–critical environments, including operational and computational constraints, should be addressed. One interpretation is that the findings contribute to the theoretical understanding of process control and could provide practitioners with guidance on combining advanced and classical methods. In this context, hybrid control models might offer a viable means of integrating transparency, robustness, and predictive capability.