This paper presents a synergistic cloud detection algorithm that has been developed for processing simultaneous observations from AATSR and MERIS sensors on-board ENVISAT. The main objective of this work is to explore sensor synergies in order to increase the cloud detection accuracy and provide a reliable cloud mask. This is of paramount importance in the framework of the ESA climate change initiative for clouds (Cloud CCI), which aims to provide long time series of cloud properties at a global scale from satellite data. The cloud detection algorithm is based on an ensemble of artificial neural networks, where the outputs of different dedicated models are combined to provide more accurate and robust predictions. The performance of the method has been tested on a large number of real images, and provides higher classification accuracy than other methods, especially when spatial information from the images is included in the classifiers.