Operating hydro turbines in off-design conditions increases the risk of cavitation occurrence, which in turn, leads to numerous problems such as performance degradations, structural vibrations, and most importantly, mechanical damage due to erosion. It is therefore crucial to develop a monitoring system that detects the occurrence and severity of cavitation in real time. For this purpose, a cavitation detection methodology has been developed that is based on the analysis of acoustic emissions of a turbine with machine learning algorithms. In this method, a conventional microphone is used to record the airborne noise emitted from a turbine under different working conditions, and then, a supervised learning algorithm is trained to classify the recorded noise signals into cavitating and non-cavitating categories. The detection system was developed based on laboratory tests and was validated in Ernen hydropower plant located in Canton of Wallis in southeast of Switzerland. This power plant consists of two identical double-flux Francis turbines each having a maximum power of 16 MW and a net head of 270 mWC. The preliminary results obtained from a two-day experimental campaign in the Ernen powerplant are very promising in terms of cavitation detection with a classification accuracy of more than 90 %. The system could be implemented either for real-time monitoring of cavitation occurrence allowing the operators to avoid such a condition or as a post processing tool to evaluate the number of hours a turbine has worked under severe conditions. Work is still ongoing to deploy more complex learning algorithms for this task to minimize expert intervention and/or interpretation during the setup process.