Outliers are observations that do not follow the statistical distribution of the bulk of the data, and consequently may lead to erroneous results with respect to statistical analysis. Many conventional outlier detection tools are based on the assumption that the data is identically and independently distributed. In this paper, an outlier-resistant data filter-cleaner is proposed. The proposed data filter-cleaner includes an on-line outlier-resistant estimate of the process model and combines it with a modified Kalman filter to detect and "clean" outliers. The advantage over existing methods is that the proposed method has the following features: (a) a priori knowledge of the process model is not required; (b) it is applicable to autocorrelated data; (c) it can be implemented on-line; and (d) it tries to only clean (i.e., detects and replaces) outliers and preserves all other information in the data. (C) 2004 Elsevier Ltd. All rights reserved.