Hyperspectral imaging (HSI) techniques are useful for obtaining very detailed structural and compositional information from biomedical, pharmaceutical, or clinical samples, among others. The informative value of these methods can be further increased through the application of different HSI techniques and joint analysis of the data. However, interpretation and understanding of multimodal HSI have been impeded by difficulties in registration of the different HSI data sets and by the lack of integrative analysis concepts. Here, we introduce two-dimensional correlation spectroscopy (2DCOS) as a novel technique for jointly analyzing HSI data which allows one to obtain deeper insights into the chemistry of complex samples by decrypting auto- and heterospectral correlations that may exist between features of the different HSI data. The general workflow of 2DCOS analysis is demonstrated by HSI examples acquired from cryo-sections of hamster brain tissue using Fourier-transform infrared (FT-IR) microspectroscopy, confocal Raman microspectroscopy (CRM), and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Multimodal hyperspectral image analysis by 2DCOS opens up new opportunities for spectral band assignments and thus the interpretation of structure-spectra and composition-spectra relationships. We foresee wide application potential for describing complex samples in various fields ranging from biomedicine to industrial applications.