Non-destructive measurement of soluble solid content in Gannan navel oranges was carried out by visible/near-infrared spectroscopy detection method. Effective information of spectra was obtained by principal component analysis, and was used as the input variables of artificial neural network for building the nonlinear model. The results, based on calibration for 90 samples, are 0.9147 and 0.5203 for calibration correlation coefficient and root mean square error of calibration. The results, based on prediction for 38 unknown samples, are 0.9033, 0.6964 and 4.5709% for prediction correlation coefficient, root mean square error of prediction, and relative standard deviation (RSD), respectively. Experimental results show that visible/near-infrared spectroscopy detection method, based on artificial neural network, for non-destructive measurement of soluble solid content in Gannan navel oranges is feasible.