This paper provides an up-to-date survey of multiple classifier systems (MCS) from the perspective of Hybrid Intelligent Systems. It discusses major issues such as diversity and decision fusion methods, providing a comprehensive overview of the spectrum of applications currently being developed. The authors highlight the advantages of MCS, including their ability to handle data scarcity and abundance, outperform individual classifiers, and exploit diverse modeling approaches. The paper also covers system topology, ensemble design, fuser design, and the handling of concept drift. Recent applications in remote sensing, computer security, financial risk assessment, fraud detection, and medical diagnosis are reviewed, showcasing the practical relevance and effectiveness of MCS in various domains.This paper provides an up-to-date survey of multiple classifier systems (MCS) from the perspective of Hybrid Intelligent Systems. It discusses major issues such as diversity and decision fusion methods, providing a comprehensive overview of the spectrum of applications currently being developed. The authors highlight the advantages of MCS, including their ability to handle data scarcity and abundance, outperform individual classifiers, and exploit diverse modeling approaches. The paper also covers system topology, ensemble design, fuser design, and the handling of concept drift. Recent applications in remote sensing, computer security, financial risk assessment, fraud detection, and medical diagnosis are reviewed, showcasing the practical relevance and effectiveness of MCS in various domains.