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Since the dawn of mankind's introduction to powered flights, there have beenmultiple incidents which can be attributed to aircraft stalls. Most modern-dayaircraft are equipped with advanced warning systems to warn the pilots about apotential stall, so that pilots may adopt the necessary recovery measures. Butthese warnings often have a short window before the aircraft actually enters astall and require the pilots to act promptly to prevent it. In this paper, wepropose a deep learning based approach to predict an Impending stall, well inadvance, even before the stall-warning is triggered. We leverage thecapabilities of long short-term memory (LSTM) recurrent neural networks (RNN)and propose a novel approach to predict potential stalls from the sequentialin-flight sensor data. Three different neural network architectures wereexplored. The neural network models, trained on 26400 seconds of simulatorflight data are able to predict a potential stall with > 95% accuracy,approximately 10 seconds in advance of the stall-warning trigger. This cansignificantly augment the Pilot's preparedness to handle an unexpected stalland will add an additional layer of safety to the traditional stall warningsystems.

LSTM recurrent neural network assisted aircraft stall prediction for enhanced situational awareness