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Recently analysis of variance (ANOVA)-simultaneous component analysis (ASCA) has been introduced as an explorative tool for the analysis of multivariate data with an underlying experimental design [Smilde et al. Bioinformatics 2005; 21: 3043–3048]. This paper focuses on the general methodological framework of ASCA. The drawbacks of other methods for the analysis of this type of data are discussed, as well as the advantages of ASCA above these other methods. Three case studies are used to illustrate the use of ASCA. The relationship between ASCA and several other multivariate data analysis techniques is demonstrated. Finally, possible extensions for ASCA are presented, including multiway analysis and multivariate regression. Copyright© 2006 John Wiley & Sons, Ltd.