Wood is our most important renewable resource. We need better quality and quantity both according to the wood itself and the processes that are using wood as a raw material. Hence, the understanding of the chemical composition of the wood is of high importance. Improved and new methods for analyzing wood are important to achieve better knowledge about both refining processes and raw material. The combination of NMR and multivariate analyses (MVA) is a powerful method for these analyses but so far it has been limited mainly to 1D NMR. In this project, we have developed methods for combining 2D NMR and MVA in both wood analysis and metabolomics. This combination was used to compare samples from normal wood and tension wood, and also trees with a down regulation of a pectin responsible gene. Dissolving pulp was also examined using the same combination of 2D-NMR and MVA, together with FT-IR and solid state 13C CP-MAS NMR. Here we focused on the difference between wood type (softwood and hardwood), process type (sulfite and sulfate) and viscosity. These methods confirmed and added knowledge about the dissolving pulp. Also reactivity was compared in relation to morphology of the cellulose and pulp composition. Based on the method and software used in the wood analysis projects, a new method called HSQC-STOCSY was developed. This method is especially suited for assignment of substances in complex mixtures. Peaks in 2D NMR spectra that correlate between different samples are plotted in correlation plots resembling regular NMR spectra. These correlation plots have great potential in identifying individual components in complex mixtures as shown here in a metabolic data set. This method could potentially also be used in other areas such as drug/target analyses, protein dynamics and assignment of wood spectra.