Blueberry is becoming increasingly popular because consumers like its flavor and antioxidant capacity for anti-aging. Firmness and soluble solids content (SSC) are the most important internal quality attributes for blueberries, and they are directly related to the maturity/ripeness and shelf life. The current blueberry firmness inspection systems are based on the mechanical vibration or impact principle, which could induce tissue damage, while computer vision systems only inspect external quality (i.e., size and color). This study reports on the prediction of firmness and SSC, using hyperspectral reflectance imaging in the region of 500-1,000 nm. A line scan system was used to acquire hyperspectral images of 300 blueberries for two fruit orientations (i.e., stem and calyx ends). Mean spectra were extracted from the regions of interest for the hyperspectral images of each blueberry. Prediction models were developed based on partial least squares method using cross validation and were externally tested with 25% of the samples. Better firmness predictions (R = 0.87) were obtained, compared to SSC predictions (R = 0.79). Fruit orientation had no or insignificant effect on firmness and SSC prediction. Further analysis showed that blueberries could be sorted into two classes of firmness. This research has demonstrated the feasibility of hyperspectral imaging technique for sorting and grading blueberries to enhance their quality and marketability.