It is highly desirable to have a statistical process monitoring (SPM) system that detects the abnormalities in process operations quickly with as few missed and false alarms as possible while the process operates under various operating conditions An agent-based combined monitoring and fault detection framework is proposed in this study. In this framework, different SPM techniques compete with and complement each other to enhance detection speed and accuracy. SPM techniques from literature such as principal component analysis (PCA), multiblock PCA (MBPCA), and dynamic PCA (DPCA) techniques are implemented in this agent-based process supervision system An agent performance assessment and agent management layer provides dynamic adaptation of the supervision system and improves the performance of SPM The statistical information coming from each of the statistical techniques is summarized through a consensus mechanism The performance of the agent-based consensus mechanism using different consensus criteria is tested for system disturbances of various magnitudes The effectiveness of the proposed agent-based framework with different consensus criteria is evaluated based on fault detection times and missed alarm rates and the adaptation of the supervision system is illustrated