Soft sensors are widely used to estimate process variables that are difficult to measure online. By using soft sensors, analyzer faults can be detected by estimation errors. However, it is difficult to detect abnormal data and determine the reasons because estimation errors increase not only due to analyzer faults but also due to variations caused by changes in the state of chemical plants. To separate those factors, we previously proposed to construct the relationships between distances to soft sensor models (DMs) and the accuracy of prediction of the models quantitatively and estimate the prediction accuracy of new data online. In this article, we used a one-class support vector machine (OCSVM) to estimate data density and the output of an OCSVM as a DM. The proposed method was applied to real industrial data and the superiority of the proposed DM to the traditional ones was demonstrated by comparing their results. (c) 2013 American Institute of Chemical Engineers AIChE J, 59: 20462050, 2013