Multivariate curve resolution (MCR) and 2D correlation spectroscopy (2D-CoS), including sample-sample correlation, have been applied to the analysis of evolving midinfrared spectroscopic data sets obtained from titrations of organic acids in aqueous solution. In these data sets, well-defined species with significant differences in their spectra are responsible for the spectral variation observed. The two fundamentally different chemometric techniques have been evaluated and discussed on the basis of experimental and supportive simulated data sets. MCR gives information that can be directly related to the chemical species that is of importance from a practical point of view, whereas 2D-CoS results normally require more interpretation. The obtained conclusions are regarded valid for similar evolving data, which are increasingly being encountered in analytical chemistry when multivariate detectors are used to follow dynamic processes, including separations as well as chemical reactions, among others.