To pursue optimal comprehensive economic benefit, the assessment on the operational performance of the process becomes more and more critical for industrial processes. In this study, a novel assessment method regarding the operating optimality as well as the cause identification strategy are proposed for nonlinear industrial processes based on nonlinear optimality related variation information (NORVI). The proposed method is committed to extracting the NORVI from each performance grade and using it to establish the assessment model. Since the process variation information unrelated to operating optimality is abandoned in evaluation, the accuracy of the assessment results is improved. Additionally, owing to only the process data used in developing the assessment model, the time-consuming data alignment work is avoided, which improves the application efficiency of the proposed algorithm. Furthermore, based on the similarities between the NORVI of the test data and those of the modeling data of each performance grade, the actual operational performance of the process can be evaluated in real time. For the nonoptimal performance grade, the idea of variable selection is used to develop the cause identification strategy. Finally, the efficiency of the proposed method is illustrated with a case of gold hydrometallurgical process. (C) 2016 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.