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The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tropics and the extratropics. Forecasting extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging due to a poor understanding of the phenomena that can increase predictability on this time scale. Here we show that two artificial neural networks (ANNs), a feed-forward neural network and a recurrent neural network, allow a very competitive MJO prediction. While our average prediction skill is about 26–27 days (which competes with that obtained with most computationally demanding stateof-the-art climate models), for some initial phases and seasons the ANNs have a prediction skill of 60 days or longer. Furthermore, we show that the ANNs have a good ability to predict the MJO phase, but the amplitude is underestimated. ; This work received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska Curie Grant Agreement No 8138444. C.M. also acknowledges funding by the Spanish Ministerio de Ciencia, Innovacion y Universidades (PGC2018-099443-B-I00), and the ICREA ACADEMIA program of Generalitat de Catalunya. We would like to thank Laura Ferranti, Linus Magnusson, and Nikolaos Mastrantonas for their expertise and useful discussions. Portions of this work are modifications based on work created and shared by Google and used according to terms described in the Creative Commons 4.0 Attribution License. ; Peer Reviewed ; Postprint (published version)