Abstract： Complex chemical processes often have multiple operating modes to meet the changes of production condition. The actual industrial processes often contain multiple operating modes and the process data is no longer solely Gaussian or nonGaussian distribution.The multimode characteristics and the uncertainty of data distribution within one single mode make the conventional multivariate statistical process monitoring methods unsuitable for fault detection.To solve the problem of multiple operating modes and complex data distribution, this paper proposed a novel multimode data processing method called local neighborhood standardization (LNS) and local density factor. The LNS was used in data preprocessing and the local density factor was used as a monitoring statistic value. The validity and effectiveness of the proposed method were illustrated through a numerical example and the Tennessee Eastman process.