We analyze potential causes of anomalies, as they vary from incipient system failures to malfunctioning sensors, operating the asset in unusual regions, using inappropriate anomaly detection models, etc. For each cause, we follow the PHM cycle, creating an anomaly resolution action. Within this systematic approach, we focus on one of the most neglected causes for anomalies: the inadequate accuracy of anomaly detection models. We describe a hybrid approach based on a fuzzy supervisory system and an ensemble of locally trained auto associative neural networks (AANN’s). The supervisory system will manage the transition among local AANN’s during operating regime changes. This approach is illustrated with experiments with a simulated aircraft engine.