A number of limitations have inhibited the success of batch process monitoring:- the finite and variable duration of a batch, the presence of significant non-linearities, the lack of on-line sensors for measuring quality variables, the absence of steady-state operation, the difficulty of developing accurate mechanistic models and process measurements that are autocorrelated in time as well as being correlated with one another. Recent approaches to the monitoring of batch behaviour have been based on extensions of the statistical projection methods of Principal Components Analysis (PCA) and Projection to Latent Structures (PLS)- multiway PCA and multiway PLS. These techniques form the bases of the multivariate statistical process control charts for batch process monitoring. The control limits for detecting when a process is moving out of control for multivariate SPC charts are based upon Hotelling’s T-2 statistic. A new approach which allows the nominal data to dictate the form and shape of the bound, the M(2) statistic, is reviewed. Finally, an application of multivariate SPC and the impact the different confidence bounds have on process operation is highlighted by application to a batch methyl methacrylate polymerisation reactor.