Historically, soil quality and function used to be assessed through routine soil chemical and physical analysis in the lab. Standard procedures for measuring soil properties are rather complex, costly and time-consuming. A rapid economical soil analytical technique is needed as there is a great demand for larger amounts of good quality, inexpensive soil data available for use in environmental monitoring, modeling and precision agriculture. In this paper possibility of predicting soil organic matter (SOM) content from measured reflectance spectra is studied using multiple linear stepwise regression (MLSR) and artificial neural network (ANN). After preprocessing of the primitive spectrum, some hyperspectral models for predicting SOM are built up with the aid of MLSR and ANN, and verified by a validation set. Performance of these two adaptive methods is compared in order to examine linear and non-linear relationship between soil reflectance and SOM content. Results show that to a certainty, both methods have some potential for application in estimating SOM. Performance indexes from both methods suggest ANN models are better than regression models, and the BP integrated model is better than the single BP model. Integrating the ANN subnets is a valid method for improving accuracy and stability of SOM retrieval. The ANN integrated model with the root mean square error (RMSE) of 1.31 is the best model in this research, which can be used in rapid acquisition of SOM content.