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Recently, developments of process monitoring system in order to detect and diagnose process abnormali-ties has got the spotlight in process systems engineering. Normal data obtained from processes provide available infor-mation of process characteristics to be used for modeling, monitoring, and control. Since modern chemical andenvironmental processes have high dimensionality, strong correlation, severe dynamics and nonlinearity, it is not easy toanalyze a process through model-based approach. To overcome limitations of model-based approach, lots of systemengineers and academic researchers have focused on statistical approach combined with multivariable analysis such asprincipal component analysis (PCA), partial least squares (PLS), and so on. Several multivariate analysis methods havebeen modified to apply it to a chemical process with specific characteristics such as dynamics, nonlinearity, and so on.This paper discusses about missing value estimation and sensor fault identification based on process variable reconstruc-tion using dynamic PCA and canonical variate analysis.