In this work, a new method to derive hourly global horizontal irradiance (GM) estimates from Meteosat Second Generation (MSG) imagery is presented. The method is based on an optimized Artificial Neural Network (ANN) ensemble model using a selection of the best ANN models identified from an initial ensemble that discerns between different sky conditions and an additional ensemble that considers all sky conditions together. For benchmarking purposes, hourly GHI estimates computed with the Heliosat-2 method, accounting for the diurnal variability of ground albedo, are used. Data collected during the 3-year period from 2009 to 2011 at 28 radiometric stations located in northern Africa, Middle East and Europe, are used in the procedure. From these stations, 7 are used to train the ANN models and the other 21 for independent validation. Results obtained with the proposed ANN ensemble model reduced the RMSE value of the Heliosat-2 model a 22% for all-sky conditions and a 42% for overcast conditions. (C) 2015 Elsevier Ltd. All rights reserved.