Publisher Summary Conventional approaches assume that the only available information about the process is the known model constraints. A wealth of information is available in the operating history of the plant. Together with spatial redundancy, there is also temporal redundancy—that is, temporal redundancy exists when measurements at different past times are available. This temporal redundancy contains information about the measurement behavior, such as the probability distribution. This chapter discusses methods that try to exploit these ideas by formulating the reconciliation problem in a different way. Correlations are inherent in chemical processes even where it can be assumed that there is no correlation among the data. Principal component analysis (PCA) transforms a set of correlated variables into a new set of uncorrelated ones, known as “principal components,” and is an effective tool in multivariate data analysis. The chapter describes a method that combines PCA and the steady-state data reconciliation model to provide sharper statistical tests for gross errors.