This paper presents a virtual sensor for helicopter Main Gear Box (MGB) oil pressure. It is developed for anticipating failures of systems related to MGB lubrication. The virtual sensor is built using Machine Learning and massive in-service flight data collected from Airbus helicopters flying worldwide. The correlation between oil pressure values and other flight parameters is learnt during stable phases of flights in which the system is in its nominal state. At each flight, the values continuously estimated by the virtual sensor are compared to the measured ones, and an alert is raised when the difference becomes higher than a statistically predefined threshold. The virtual sensor was tested using normal and abnormal flights, and the results obtained so far in terms of anomaly detection performance are promising.