Abstract: With the rapid development of science and technology,the increasing requirements for modern industry have been receiving more and more attentions. The traditional multivariate statistical process monitoring (MSPM) methods, e.g., principal component analysis (PCA) and partial least square (PLS), have been widely studied. However, the data collected in complex chemical processes are often mixed with process noise. It is a hot topic in industry process monitoring how to reduce redundant data and extract the effective information of data. By fusing sequence correlation with low rank representation (LRR), this paper proposes an information extraction algorithm, sequence low rank embedding (SLRE). Firstly, the training samples are decomposed by LRR, which can divide the training data matrix into a low rank part and a sparse residual part. The sparse residual part contains most of the noise of the data. By removing this part, the noise points can be largely eliminated from the sample data and the redundant information in the data can be removed such that the robustness of the algorithm can be enhanced. Meanwhile, by calculating the inter-sample correlation coefficient, the weighted matrix is constructed such that the global-local characteristics of the data can be maintained. Moreover, local weighting can effectively guarantee the integrity of the data information in the modeling and fully extract the information contained in the training dataset. Furthermore, the embedding algorithm is used to achieve the reduction of data dimensionality. And then, the T² and SPE statistics are established and the control limit is estimated using the kernel density estimation (KDE) method. Finally, the proposed SLRE algorithm is tested via a numerical example and the Tennessee Eastman process. It is shown from the experimental results that SLRE is feasible and effective for dealing with the problem of data noise and information redundancy, by which the characteristics of low dimension and low rank can be ensured and a more accurate process monitoring model can be obtained.