Novel ways of using multivariate statistical methods to develop process models for on-line monitoring and control are proposed. On a binary distillation column, PLS is used to develop a regression estimation using multiple tray temperature measurements and a manipulated variable to estimate and control distillate composition. Additionally, a feedback controller design based on a static PCA/PCR model is developed and demonstrated on the binary column. This controller's performance is compared with a PI controller for disturbance rejection and setpoint tracking. On a real-world chemical process, it is shown how both PLS and PCS are necessary to model normal plant operations. These models permit real-time monitoring and detection in a reduced subspace defined by the statistical independent variations in the data. Techniques for real-time monitoring and fault detection are demonstrated.