Pork quality is usually evaluated subjectively based on color, texture and exudation characteristics of the meat. In this study, a hyperspectral imaging-based technique was evaluated for rapid, accurate and objective assessment of pork quality. In addition, marbling level was also automatically determined. The system was able to extract spectral characteristics of pork samples. Appropriate spatial features were obtained for marbling distribution in pork meat. Existing marbling standards were scanned, and indices of the marbling scores were formulated by co-occurrence matrix. The principal component analysis (PCA) method was used to compress the entire spectral wavelengths (430-1000 nm) into 5, 10 and 20 principal components (PCs), which were then clustered into quality groups. Artificial neural network was used to classify these groups. Results showed that reddish, firm and non-exudative (RFN) and reddish, soft and exudative (RSE) samples were successfully grouped; the total corrected ratio was 75-80%. The feed-forward neural network model yielded corrected classification as 69% by 5 PCs and 85% by 10 PCs. Angular second moment was successfully used to determine marbling scores excepting the score at 10.0. Forty samples were sorted and the result showed that the samples' marbling score ranged from 3.0 to 5.0. (c) 2007 Elsevier Ltd. All rights reserved.