Fluid viscosity is one of the most important parameters necessary to establish reservoir production and economical potential. Until the appearance of NMR techniques in the oil industry, oil viscosity determination was limited to laboratory tests and correlations with API gravity. Nuclear Magnetic Resonance is a technique based on the magnetic behavior of hydrogen nuclei. This behavior is the consequence of fluid properties, as viscosity and density, and its interactions with its surroundings. The use of NMR signals for estimation of fluid viscosity has been based mainly on correlations with single NMR parameters as logarithmic (T2log) and geometric averages (T2geo). However, qualitative analysis of NMR T2 distributions indicate that changes on NMR patterns translate on changes on viscosity, that sometimes are not reflected on the averages used. This fact brought the idea of developing a multivariable model, which considers the use of all points of the NMR T2 distribution to enhance fluid properties estimation. The use of neural network technique was identified and several models were developed. The models were developed using the T2 distribution and the cumulative T2 distribution. The model constructed based on the cumulative T2 distribution, showed a better prediction of oil viscosity, incrementing the correlation with real values from 64% using the T2log correlation to 87% with the neural network. This work was done on 24 oil samples from different Venezuelan fields covering a range from 6 to 700 cP. An additional model was developed selecting 15 samples from the same field covering a range from 25 to 72 cP. Comparing the results of the model vs. the estimation through T2log correlations, the prediction was enhanced from 89% to 99%, creating an excellent model for fluid viscosity determinations through NMR signals.