The quest for a complete understanding of mixtures is a challenge which has stimulated the development of several techniques. One of the most powerful NMR-based techniques is known as Diffusion-Ordered SpectroscopY (DOSY), in which it is possible to distinguish the NMR spectra of chemical species with different hydrodynamic radii, i.e. with different self-diffusion coefficients. It allows the study of intact mixtures, providing information on the interactions within the mixture and saving time and money compared to other techniques. Unfortunately, DOSY is not very effective when signals overlap and/or the diffusion coefficients are very similar. This drawback has led to the development of new methods to overcome this problem. The present investigation is focused on developing some of these. Most DOSY datasets show multiplet phase distortions caused by J-modulation. These distortions not only hinder the interpretation of spectra, but also increase the overlap between signals. The addition of a 45º purging pulse immediately before the onset of acquisition is proposed as a way to remove the unwanted distortions. Most DOSY experiments use 1H detection, because of the higher sensitivity which is generally achieved. However, acquiring spectra with other nuclei such as 13C can reduce overlap problems. Two new sequences have been developed to maximize the sensitivity of heteronuclear DOSY experiments. In order to increase resolving power, it is also possible to incorporate another variable into diffusion experiments as a further dimension. If this results in an approximately trilinear dataset (each dimension varying independently), it is possible to extract physically meaningful information for each component using multivariate statistical methods. This is explored for the cases where the new variable is either the relaxation behaviour or the concentration variation (which can be measured during a reaction or in a set of samples with different concentrations for each component). PARAllel FACtor (PARAFAC) analysis can obtain the spectra, diffusional decay and relaxation evolution or kinetics for each of the components. In a completely different approach, the separation power of liquid chromatography has been combined in a novel way with the NMR potential for elucidating structures. NMR has been used previously as a precise way to measure average flow velocities, even in porous media. Using this capability to detect the different average velocities of solutes that occur in chromatographic columns ought to provide a new way of analysing mixtures with the same potential as LC-NMR, but faster and more simple. In such a flow system, a chromatographic column is introduced into the NMR probe and a 2D dataset is acquired and Fourier transformed to obtain the velocity distribution for each of the detected NMR signals.