Sensor fault detection and isolation (SFDI) approaches, based on support vector regression (SVR) plant sensor models and self-organizing-map (SOM) analysis, were investigated for application to reverse osmosis (RO) desalination plant operation. SFDI-SVR and SFDI-SOM were assessed using operational data from a small spiral-wound RO pilot plant and synthetic faulty data generated as perturbations relative to normal plant operational data. SFDI-SVR was achieved without false negative (FN) detections for sensor deviations of >=vertical bar 10%vertical bar and FN detections of, at the most, less than or similar to broken vertical bar 5%broken vertical bar, and for sensor deviations of greater than or similar to vertical bar 4%vertical bar at sensor fault detection (FD) thresholds of up to similar to vertical bar 4%vertical bar. False positive (FP) detections were almost invariant, with respect to sensor FD, being less than or similar to vertical bar 5%vertical bar for sensor deviations of greater than or similar to vertical bar 5%vertical bar. Corrections of faulty sensor readings were within SVR model accuracy (AARE < 1%) for SFDI-SVR and less than or similar to vertical bar 5%vertical bar for SFDI-SOM. Although SFDI-SOM has lower detection accuracy, it requires a single overall plant model (or SOM), while providing pictorial representation of plant operation and depiction of faulty operational trajectories.