Data analysis plays an important role in system modeling, monitoring and optimization. Among those data analysis techniques, change point detection has been widely applied in various areas including chemical process, climate monitoring, examination of gene expressions and quality control in the manufacturing industry, etc. In this paper, an Expectation Maximization (EM) algorithm is proposed to detect the time instants at which data properties are subject to change. The problem is solved in the presence of unknown and changing mean and covariance in process data. Performance of the proposed algorithm is evaluated through simulated and experimental study. The results demonstrate satisfactory detection of single and multiple changes using EM approach. (C) 2014 Elsevier Ltd. All rights reserved.