Data sets resulting from metabolomics, proteomics, or metabolic profiling experiments are usually complex. This type of data contains underlying factors, such as time, doses, or combinations thereof. Classical biostatistics methods do not take into account the structure of such complex data sets. However, incorporating this structure into the data analysis is important for understanding the biological information in these data sets.We describe ANOVA simultaneous component analysis (ASCA), a method capable of dealing with complex multivariate data sets containing an underlying experimental design. It is a generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a data set from a metabolomics experiment with time and dose factors.