The fusion of different information sourcesis a persistent and intriguing issue. It has been addressed for centuries in various disciplines, including political science, probability and statistics, system reliability assessment, computer science, and distributed detection in communications. Early seminal work on fusion was c- ried out by pioneers such as Laplace and von Neumann. More recently, research activities in information fusion have focused on pattern recognition. During the 1990s, classifier fusion schemes, especially at the so-called decision-level, emerged under a plethora of different names in various scientific communities, including machine learning, neural networks, pattern recognition, and statistics. The d- ferent nomenclatures introduced by these communities reffected their different perspectives and cultural backgrounds as well as the absence of common forums and the poor dissemination of the most important results. In 1999, the first workshop on multiple classifier systems was organized with the main goal of creating a common international forum to promote the diss- ination of the results achieved in the diverse communities and the adoption of a common terminology, thus giving the different perspectives and cultural ba- grounds some concrete added value. After five meetings of this workshop, there is strong evidence that significant steps have been made towards this goal. - searchers from these diverse communities successfully participated in the wo- shops, and world experts presented surveys of the state of the art from the perspectives of their communities to aid cross-fertilization.