The rapid growth in the number of aircraft and pilots emphasises the need for an AI-enabled training framework that can offer precise, automated examination of flight manoeuvres. This will be useful in optimising the pilot's training efficiency and minimising iterations of the conduct of flight manoeuvres, thereby reducing the training time of the pilot for a flight. A general framework is developed that can be used for all kinds of flight phases and aircraft types. A pre-trained machine learning model is designed using a supervised learning technique, Random Forest, to recognise different manoeuvres. Various statistical parameters, such as mean, standard deviation, kurtosis, skewness, etc., of several flight parameters were used as the input features to train the Random Forest classifier. In the present work, the classifier is trained using several actual flight test data manoeuvres, and is also supplemented with simulated manoeuvres. The achieved gross accuracy for manoeuvre recognition is approximately 96%. The developed Automatic Flight Manoeuvre Recognition module (AFMR) is integrated into the pilot-in-the-loop simulator for evaluation. This approach can be adopted for various aircraft programs for a wide range of flight manoeuvres to aid pilots in reducing their training time in the simulator as well as actual flights.