There exist many algorithms for control performance monitoring. There are also many algorithms available for process monitoring. There are, however, few methods available for synthesis of various monitoring techniques to form a diagnosing system for optimal decision making. This paper is concerned with establishing and demonstrating a novel probabilistic diagnostic framework for control loop monitoring. The new framework possesses a number of desired properties, including, for example, quantitative probabilistic diagnosis, flexibility in synthesizing different monitoring techniques, robustness in the presence of missing data or missing variables, ease of expanding or shrinking the diagnosing system, ability to incorporate a priori process knowledge, and capability for decision making. As the backbone of the proposed framework, the emerging Bayesian methods are introduced and shown to be the appropriate tools for solving the problem of concern. Several representative control loop diagnostic problems are formulated under the Bayesian framework and their solutions are demonstrated through examples. The experiences and challenges learned from industrial applications of Bayesian methods are summarized and some of future research directions are discussed. (C) 2008 Elsevier Ltd. All rights reserved.