The electrical submersible pump (ESP) is currently the fastest growing technology for artificial-lift pumping in oil production. However, ESP performance often declines and can reach the point of service interruption due to factors like high gas volumes, high temperature, and corrosion, among others. The financial impact of ESP failure is substantial, from both lost production and replacement costs. Therefore, ESP performance in extensively monitored, and numerous workflows exist to suggest actions in case of breakdowns. However, such workflows are reactive in nature, i.e., action is taken after tripping or failure. Furthermore, given the emerging trend in the oil industry of using downhole sensors for real-time surveillance of parameters impacting ESP performance, there is an opportunity for predicting and preventing ESP shutdowns using data analytics. Therefore, a datadriven analytical methodology is proposed to advance towards a proactive approach to ESP health monitoring based on predictive analytics to detect impending problems, diagnose their cause, and prescribe corrective or preventive action. The methodology is based on using multivariable statistics, and can be easily automated on a computer. The main benefits from this methodology are that warnings or alarms can be issued well ahead of an abnormal situation; if an abnormal situation occurs, diagnosis (likely causes) can be automatically offered; and measures can be proposed to correct the problem and possibly prevent it from happening in the future.