In many chemical processes, variables which indicate product quality are infrequently and irregularly sampled. Often, the inter-sample behavior of these quality variables can be inferred from manipulated variables and other process variables which are measured frequently. When the quality variables are irregularly sampled, maximum likelihood estimation (MLE) of the model parameters can be performed using the expectation maximization (EM) approach. A state-space model identification procedure based on the EM algorithm yields a Kalman filter-based prediction-correction mechanism which can be used for optimal prediction of the quality variables. In this paper, we describe such a state-space model identification and estimation method and present the results of its application on simulation, laboratory-scale and industrial case studies. © 2005 Elsevier Ltd. All rights reserved.