In this paper, we present some recent developments of Multiple Classifiers Systems (MCS) for remote sensing applications. Some standard MCS methods (boosting, bagging, consensus theory and random forests) are briefly described and applied to multisource data (satellite multispectral images, elevation, slope and aspect data) for landcover classification. In the second part, special attention is given to Support Vector Machines (SVM) based algorithms. In particular, the fusion of two classifiers using both spectral and the spatial information is discussed in the frame of hyperspectral remote sensing for the classification of urban areas. In all the cases, MCS provide a significant improvement of the classification accuracies. In order to address new challenges for the analysis of remote sensing data, MCS provide invaluable tools to handle situations with an ever growing complexity. Examples include extraction of multiple features from one data set, use of multi-sensor data, and complementary use of several algorithms in a decision fusion scheme.