[1] For characterizing water flow in the vadose zone, the water retention curve (WRC) of the soil must be known. Because conventional WRC measurements demand much time and effort in the laboratory, alternative methods with shortened measurement duration are desired. The WRC can be estimated, for instance, from the cumulative pore size distribution (PSD) of the investigated material. Geophysical applications of nuclear magnetic resonance (NMR) relaxometry have successfully been applied to recover PSDs of sandstones and limestones. It is therefore expected that the multiexponential analysis of the NMR signal from water-saturated loose sediments leads to a reliable estimation of the WRC. We propose an approach to estimate the WRC using the cumulative NMR relaxation time distribution and approximate it with the well-known van-Genuchten (VG) model. Thereby, the VG parameter n, which controls the curvature of the WRC, is of particular interest, because it is the essential parameter to predict the relative hydraulic conductivity. The NMR curves are calibrated with only two conventional WRC measurements, first, to determine the residual water content and, second, to define a fixed point that relates the relaxation time to a corresponding capillary pressure. We test our approach with natural and artificial soil samples and compare the NMR-based results to WRC measurements using a pressure plate apparatus and to WRC predictions from the software ROSETTA. We found that for sandy soils n can reliably be estimated with NMR, whereas for samples with clay and silt contents higher than 10% the estimation fails. This is the case when the hydraulic properties of the soil are mainly controlled by the pore constrictions. For such samples, the sensitivity of the NMR method for the pore bodies hampers a plausible WRC estimation. Citation: Costabel, S., and U. Yaramanci (2013), Estimation of water retention parameters from nuclear magnetic resonance relaxation time distributions, Water Resour. Res., 49, 2068-2079, doi:10.1002/wrcr.20207.