An engine fault diagnosis tool was created. The tool uses Support Vector Machine to identify root causes of in-service events to aircraft engines. The tool was developed in Python and it allowed to automatically identify new events root causes from past events. In-service events analysed regard v2500 Airbus equipped fleet between 2010 and 2018. Accuracy of the tool was tested on new events occurred in 2018 with a precision of 87 % in identifying the correct root cause for repetitive events. Digital flight recorder data DFDR were used to study impact of events on engine performance. Multiple regression analysis was applied to DFDR data to study anomalous variations during events.