This article proposes a novel dimension reduction method of independent component analysis for process monitoring based on minimum mean square error (MSE). Firstly, the order of the independent components (ICs) is ranked according to their importance estimated by MSE, and the mathematical proof is presented. Secondly, the top-n ICs are selected as dominant components and the dimension of ICs is reduced. The sum of the squared independent scores (I-2) and the squared prediction error (SPE) are adopted as monitoring statistics. The control limits of I-2 and SPE are determined by the kernel density estimation (KDE). The proposed dimension reduction method is applied to fault detection in a simple multivariate process and the simulation benchmark of Tennessee Eastman process. Finally, two fault conditions of pulverizing system in power plant are analyzed by the proposed method. The experiments results verify the effectiveness of the proposed method. (C) 2011 Elsevier Ltd. All rights reserved.