A tiny fault in chemical plants is likely to cause an enormous accident possibly with heavy losses of personnel, property, and environment. Therefore, process monitoring is demanded to timely detect faults and identify fault variables, so as to avoid deterioration of tiny faults into accidents. Nowadays principal component analysis (PCA) is the most widely used method in chemical process monitoring practice with its simplicity and effectiveness. However, it has some drawbacks. First, it roughly assumes process data as Gaussian distribution. But sometimes it is not satisfied. Furthermore, PCA uses variance and covariance (also called Pearson correlation coefficients) as criterion to choose principal components, however they are not robust in capturing nonlinear data variation. To alleviate these problems, an improved PCA-a Johnson transformation based robust method for process monitoring (JSPCA) was proposed in this work. First, Johnson transformation was introduced to make process data obey Gaussian distribution. Second, the Spearman correlation coefficient matrix instead of covariance matrix was established to extract principal components and span feature space. Finally, process data were projected into feature space where T2 and SPE statistics were obtained for process monitoring. The proposed method had its fault detection ability tested in the benchmark TE process with comparison of PCA and KPCA. The results showed that the proposed method had higher fault detection rates than PCA and KPCA when using T2 as detection indicator. However, the proposed method with SPE as detection indicator had higher false alarm rates than PCA and KPCA. As for fault diagnosis ability, the proposed method was tested against fault 5 and fault 10 of TE process and diagnoses fault variables more precisely than PCA and KPCA. The proposed method was better than PCA and KPCA and it was worth promoting.