Meat grading has always been a research topic because of large variations among meat products. Many subjective assessment methods with poor repeatability and tedious procedures are still widely used in meat industry. In this study, a hyperspectral-imaging-based technique was developed to achieve fast, accurate, and objective determination of pork quality attributes. The system was able to extract the spectral and spatial characteristics for simultaneous determination of drip loss and pH in pork meat. Two sets of six significant feature wavelengths were selected for predicting the drip loss (590, 645, 721, 752, 803 and 850 nm) and pH (430, 448, 470, 890, 980 and 999 nm). Two feed-forward neural network models were developed. The results showed that the correlation coefficient (r) between the predicted and actual drip loss and pH were 0.71, and 0.58, respectively, by Model 1 and 0.80 for drip loss and 0.67 for pH by Model 2. The color levels of meat samples were also mapped successfully based on a digitalized Meat Color Standard.