Abstract: Dynamic principal component analysis(DPCA) is an extension of conventional principal component analysis（PCA） for dealing with multivariate dynamic data serially correlated in time.Based on the fact that the measured variables in relation to chunk monitoring of the industrial fluidized-bed reactor are highly cross-correlated and auto-correlated, this paper presents a practical strategy for chunk monitoring by adopting DPCA in order to overcome the shortcomings of the conventional method.After introducing the basic principle of DPCA, both how to determine the time lagged length of data matrix and how to calculate the nonparametric control limits when the dynamic data are not subject to the assumption of independently identically distribution(IID) were discussed.An appropriate DPCA model based on the real data from a industrial fluidized-bed reactor was built, with parallel analysis and empirical reference distribution(ERD)method to select time lagged length and control limits, respectively.During data pretreatment, data smoothing was used to reduce noise and the serial correlations to some degree.The simulation test results showed the effectiveness of the DPCA based method.