At present, the parameters of high sulfur gas purification process present timing autocorrelation characteristics, resulting in poor static multivariate statistical process monitoring for abnormal condition. An anomaly detection and diagnosis method called Dynamic Kernel Independent Component Analysis (DKICA) was proposed, which considered the timing autocorrelation of parameters. Firstly, Auto-Regression (AR) model was introduced. The model order was determined by the parameter identification to describe the timing of autocorrelation in the monitoring process. Secondly, original variables were projected to a kernel independent space, their T ² and SPE statistics were monitored to realize anomaly detection by judging whether they exceeded control limit of normal condition. Finally, the first order partial derivative of the T ² statistic to original variable was calculated, and the contribution plot was given to achieve abnormality diagnosis. The data collected from a high sulfur gas purification plant was analyzed, and the results showed the detection accuracy of DKICA was prior to that of Kernel Independent Component Analysis (KICA).