The sensitive sparse principal component analysis(SSPCA) algorithm is proposed for monitoring complex chemical process. By using the relationship between principal component analysis(PCA) and the singular value decomposition of data matrices, the convex optimization problem is presented by introducing ??2,1norm into the cost function and the regularization penality for extracting the principal components(PCs) loadings. An iterative algorithm is proposed to solve the convex optimization problem. Meanwhile, the SSPCA algorithm is capable to take into account both PCs with large scores and PCs with small scores to promote the sensitivity to the abnormal situations in the process monitoring. A case study on the Tennessee Eastman process illustrates the effectiveness of the SSPCA algorithm.