Quantitative structure-property relationships (QSPR) on a large set of descriptors are developed for the 31P NMR chemical shifts of a large set of phosphines. The data set was composed of 291 primary, secondary, and tertiary phosphines, PH3-nRn, including substituents with different steric and electronic characteristics. Multiple linear regression and computational neural networks (CNN) were employed to create the models best suited for the prediction of 31P NMR chemical shifts. A correlation equation including seven descriptors (R2 = 0.8619) is reported. A 7-5-1 CNN was developed that produced a root-mean-error of 9.6 ppm (R2 = 0.9513) for the training set, of 11.7 ppm (R2 = 0.8986) for the cross-validation set, and of 11.3 ppm (R2 = 0.9527) for an external prediction set. The CNN methods give significantly better predictions of 31P NMR chemical shifts for phosphines than the multiple linear regression approach.