This paper proposed an entropy-based performance assessment method for batch processes with non-Gaussian disturbances.Real batch processes have some intrinsic non-linear characteristics,which makes system output be non-Gaussian even though disturbances obey Gaussian distribution.In this case,minimum variance control(MVC)based performance assessment method is not applieable to batch processes.Consequently,in order to reject non-Gaussian disturbance,entropy is adopted to provide a unified controller performance index.At the same time,the mathematical model of the batch process is often too complicated to build,so the data-driven method is one of the hottest research topics in the area of modeling batch process.This paper referred to concept of “golden benchmark batch” and proposed a data driven entropy-based index for batch processes,which breaks through the limitation of traditional model-based method.The applicability of the proposed method for controller performance assessment of batch processes was illustrated through an industrial example.