The chemical and mineral processing industries need a nonlinear process monitoring method to improve the stability and economy of their processes. Techniques that are currently available to these industries are often too computationally intensive for an industrial control system, or they are too complex to commission. In this paper, we propose using supervised locally linear embedding for projection (SLLEP) as a new nonlinear process monitoring technique to solve these issues. In addition, we suggest using a commonly available tool in modern industrial control systems, a model predictive control, to solve the quadratic program of SLLEP in real-time and with minimal effort to commission. As a case study, we demonstrate that process monitoring with SLLEP can detect and diagnose the early onset of a semiautogenous grinding (SAG) mill overload. A SAG mill overload is a highly nonlinear operating situation, and we show that principal component analysis, the best-in-class technique currently used by the industry for monitoring an overload, is unable to detect the early onset of an overload.