In this paper we present a novel approach for detecting early signs for the stuck events in drilling using Deep Learning. Specifically, we adapt neural network based unsupervised learning tool called Autoencoder for anticipating the ‘stuck’ events during the drilling process. We build Autoencoders on Recurrent Neural Networks (RNNs) to model the normal drilling activity, thereby detecting the stuck incidents as anomalous activity. We conduct experiments on the actual drilling data collected from 30 field wells operated by multiple drilling sources with diverse well profiles and demonstrate that our approach obtains promising results for the stuck sign detection. Furthermore towards explaining the trained model's prediction, we present reconstruction analysis on the individual drilling parameters. © 2021 Elsevier B.V.