While most previous work in the subject of Bayesian Fault diagnosis and control loop diagnosis use discretized evidence for performing diagnosis (an example of evidence being a monitor reading), discretizing continuous evidence can result in information loss. This paper proposes the use of kernel density estimation, a non-parametric technique for estimating the density functions of continuous random variables. Kernel density estimation requires the selection of a bandwidth parameter, used to specify the degree of smoothing, and a number of bandwidth selection techniques (optimal Gaussian, sample-point adaptive, and smoothed cross-validation) are discussed and compared. Because kernel density estimation is known to have reduced performance in high dimensions, this paper also discusses a number of existing preprocessing methods that can be used to reduce the dimensionality (grouping according to dependence, and independent component analysis). Bandwidth selection and dimensionality reduction techniques are tested on a simulation and an industrial process. (c) 2014 Elsevier Ltd. All rights reserved.