Detection of pork tenderness by hyperspectral imaging technique was proposed. First, hyperspectral images of 78 pork samples were captured by hyperspectral imaging system, and the spectral region is from 400 to 1100 nm. Dimension reduction was implemented on hyperspectral data by principal component analysis (PCA) to select 3 characteristic images. Next, 4 characteristic variables were extracted by texture analysis based on gray level cooccurrence matrix (GLCM), and they are contrast, correlation, angular second moment, and homogeneity, respectively, thus 12 characteristic variables in total for 3 characteristic images. PCA was conducted on 12 characteristic variables, and 6 principal component variables were extracted as the input of the discrimination model. The detection model of pork tenderness was constructed by artificial neural network (ANN), according to the reference results of pork tenderness by WarnerBratzler method. Detection results of ANN model are 96.15% and 80.77% in calibration and prediction sets, respectively. This work shows that it is feasible to detect pork tenderness by hyperspectral imaging technique.