Control loop diagnosis has become an increasingly important tool for improving the efficiency, reliability and safety for a variety of processes. While a number of model-based diagnosis methods have been proposed, constructing models may be a difficult task. An alternative approach is to use data-driven control-loop diagnosis, a family of diagnosis methods that make use of historical data for training the diagnostic models. Bayesian methods have been applied to data-driven control loop diagnosis to combine prior process knowledge with historical data, and can be used to assign probabilities to different modes (or operation statuses) after combination. However, one difficulty with Bayesian methods is that there must be exact knowledge of the underlying mode so that the corresponding monitor readings in the historical data can be used. If there is uncertainty about the underlying mode, the mode becomes ambiguous, which Bayesian methods do not deal with. An alternative method is proposed in this paper that exploits the properties of data-driven Bayesian methods, and can be applied for diagnosis in the presence of ambiguity. The proposed method is evaluated through simulation examples as well as applied to industrial process data. (C) 2013 Elsevier Ltd. All rights reserved.