A method for offline/online performance monitoring of a batch process, comprising the steps of: obtaining archived batch process data for said batch process comprising stored process data obtained during a plurality of runs of said batch process, said archived data comprising information defining at least one batch quality attribute for each of said plurality of runs of said batch process; forming a plurality of clusters (902, 904,..., 918) by classifying said archived data for at least a portion of said plurality of runs into a plurality of classes based on said at least one batch quality attribute, wherein each said cluster (902, 904,..., 918) comprises archived data for at least two runs of said plurality of runs having the same classification, is characterized by a centroid (C 902, C 904, C 906, C 908, C 910, C 912, C 914, C 916, C918) and a set of boundary batch run profiles that are representative of a batch behavior in a respective class of said plurality of classes; building a first feature matrix using said archived data, said first feature matrix comprising a plurality of first feature vectors consisting of wavelet coefficients that are determined as an approximation of a plurality of time trajectories of a plurality of variables (v 0,..., v J); building a first multivariate statistical model MSM using a first multivariate statistical analysis method with said first feature matrix; forming a first projection by projecting said plurality of first feature vectors onto said first MSM; characterised by building a second MSM using a second multivariate statistical analysis method different from said first multivariate statistical analysis method with information obtained from said first projection; computing (328, 329) a plurality of centroids (C 902, C 904, C 906, C 908, C 910, C 912, C 914, C 916, C 918) and a plurality of boundary profiles for said plurality of clusters (902, 904,..., 918); and performing said offline/online performance monitoring of said batch process using an integrated version of said first and second MSMs, said plurality of centroids, and said plurality of boundary profiles. obtaining archived batch process data for said batch process comprising stored process data obtained during a plurality of runs of said batch process, said archived data comprising information defining at least one batch quality attribute for each of said plurality of runs of said batch process; forming a plurality of clusters (902, 904,..., 918) by classifying said archived data for at least a portion of said plurality of runs into a plurality of classes based on said at least one batch quality attribute, wherein each said cluster (902, 904,..., 918) comprises archived data for at least two runs of said plurality of runs having the same classification, is characterized by a centroid (C 902, C 904, C 906, C 908, C 910, C 912, C 914, C 916, C918) and a set of boundary batch run profiles that are representative of a batch behavior in a respective class of said plurality of classes; building a first feature matrix using said archived data, said first feature matrix comprising a plurality of first feature vectors consisting of wavelet coefficients that are determined as an approximation of a plurality of time trajectories of a plurality of variables (v 0,..., v J); building a first multivariate statistical model MSM using a first multivariate statistical analysis method with said first feature matrix; forming a first projection by projecting said plurality of first feature vectors onto said first MSM; characterised by building a second MSM using a second multivariate statistical analysis method different from said first multivariate statistical analysis method with information obtained from said first projection; computing (328, 329) a plurality of centroids (C 902, C 904, C 906, C 908, C 910, C 912, C 914, C 916, C 918) and a plurality of boundary profiles for said plurality of clusters (902, 904,..., 918); and performing said offline/online performance monitoring of said batch process using an integrated version of said first and second MSMs, said plurality of centroids, and said plurality of boundary profiles.