Online state prediction and fault detection are typical tasks in the chemical industry. In practice it often happens that some variables, important and critical for quality control, cannot be measured online due to such restrictions as cost and reliability. An uncertainty existing in real systems allows to use a probabilistic approach to online state estimation. Such an approach is proposed in this paper. Different types of information appearing in an online diagnostic system are processed via combination of algorithms subject to probability distributions. This combination of algorithms is presented as a decomposed version of Bayesian filtering. In this paper, the proposed solution is specialized for a system with mixed both continuous and discrete-valued measurements and unobserved variables. (C) 2011 Elsevier Ltd. All rights reserved.