This article examines the performance of two one-class classification-based control charts through a real industrial application. These two control charts are the kernel distance–based control chart, known as the K chart, and the k-nearest neighbour data description-based control chart, referred to as the KNN chart. We studied the effectiveness of both charts in detecting out-of-control observations in phases I and II. Furthermore, a simulation study is conducted to compare the performance of the two control charts using the average run length criterion. The results of the comparative study show that the K chart is sensitive to small shifts in mean vector, whereas the KNN chart is sensitive to moderate shifts in mean vector. In addition, the article provides the MATLAB codes for the K chart and KNN chart developed by the authors. Copyright © 2012 John Wiley & Sons, Ltd.