The traditional statistical analysis methods ignore the relations between variables. Transfer entropy could express relations between variables effectively. So this paper proposes an MPCA online monitoring method based on entropy transfer for batch process. The transfer entropy is adopted to describe the complex relations between process variables. The non-parametric kernel density estimation method which does not depend on the prior distribution of data is utilized to calculate transfer entropy to deal with the non-Gauss distribution of the process data. By constructing the transfer entropy matrix combined with the sliding window to achieve the expression of dynamic information transfer between process variables, the MPCA model is then established based on these matrices for detecting faults of batch process. The simulation results show that, compared with the traditional MSPC method, the proposed method can timely identify the faults with better accuracy.