Soluble solids content (SSC) and firmness are not only important indicators for grading of Hami melon but also characteristic factors to determine its maturity. Thus, in order to achieve automatic grading and suitable picking of Hami melon, hyperspectral imaging technology combined with different characteristic wavelengths selection methods were used to simultaneously assess SSC, firmness and maturity of Hami melon. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and CARS-SPA algorithm were used to select the characteristic wavelengths of SSC and firmness of Hami melon from MSC pretreated spectra. The full spectral variables and selected wavelength variables were used as the inputs to build SVM model for determination of the SSC, firmness and maturity of Hami melon, respectively. The results indicated that the MSC-CARS-SPA-SVM models achieved the optimal performance for SSC and firmness of Hami melon. The correlation coefficient of prediction set (Rpre) , the root mean square error of prediction (RMSEP ) and the relative prediction deviation (RPD) were 0940 4, 0402 7 and 2941 for SSC and 0825 3, 3522 and 1771 for firmness, respectively. At the same time, the full spectrum, selected characteristic wavelengths for SSC or firmness and feature fusion by the principal component analysis (PCA) were used to build SVM discriminatory models for maturity of Hami melon, respectively. The results showed that the discriminant results of CARS-PCA-SVM model was agreement with the FS-SNV-SVM model, the recognition rate of calibration set and prediction set were 95% and 94%. The research indicated that it is the feasible to use hyperspectral imaging technology combined with different characteristic wavelengths selection methods can be used to evaluate SSC, firmness and maturity of Hami melon simultaneously.