Abstract: The calibration models were established using Partial Least Squares(PLS) and Artificial neural network(ANN) techniques to relate NIR spectral data to the concentrations of available N, available P and available K in 0.9 mm dried soil. Coefficients of determination(R²) between results from chemical analysis and NIR-predicted concentrations, based on calibrations of PLS, are 0.9520 for available N, 0.8714 for available P and 0.7300 for available K, and the mean relative errors of PLS models are 3.42%, 13.40% and 7.40%, respectively. Coefficients of determination, based on calibrations of ANN, are 0.9563 for available N, 0.9493 for available P and 0.9522 for available K, the mean relative errors of ANN models are 2.67%, 6.48% and 2.27%, respectively, and the mean relative errors of test samples are 5.44%, 16.65% and 7.87%, respectively. The results show that ANN technique is better than PLS in NIRS analysis, and the NIRS method is feasible to predict the concentration of available N, but the mean relative errors of test samples for available P and available K are high relatively, therefore, further study should be done in this field.