Generally, process operating performance affects the economic benefits of enterprises, which prompts the development of process operating performance assessment. In this study, a novel operating optimality assessment and nonoptimal cause identification strategy is proposed for non-Gaussian multimode processes. Compared with the reported methods, the advantages of the proposed method are: (i) non-Gaussian distribution data is firstly considered in operating optimality assessment; (ii) besides the online assessment strategy for stable mode, that for transitional mode is developed for the first time; (iii) a novel online mode identification method is developed based on the sample posterior probabilities together with a predefined threshold, which can not only improve the accuracy of the assessment results but also reduce the risk of misclassification; (iv) the nonoptimal cause identification approach is established to identify the variables responsible for the nonoptimal performance. Finally, the effectiveness of the proposed method is demonstrated by Tennessee Eastman (TE) process. (C) 2015 Elsevier Ltd. All rights reserved.