Objective assessment of pork quality is important for meat industry application. Previous studies using spectral approaches focused on using color and exudation features for the determination of pork quality levels without considering the image texture feature. In this study, a Gabor filter-based hyperspectral imaging technique was presented to develop an accurate system for pork quality level classification. Texture features were obtained by filtering hyperspectral images with two-dimensional Gabor functions. Different spectral features were extracted from Gabor-filtered images and hyperspectral images. The principal component analysis (PCA) was used to compress spectral features over the entire wavelengths (400-1000 nm) into principal components (PCs). 'Hybrid' PCs were created by combining PCs from hyperspectral images with PC(s) from Gabor-filtered images. Both K-means clustering and linear discriminant analysis (LDA) were applied to classify pork samples. Results showed that, the accuracy of K-mean clustering analysis reached 78% by 5 hybrid PCs and 83% by 10 hybrid PCs, which were 15% and 28% higher than the results without using texture features. The highest classification accuracy using LDA reached 100% by 5 hybrid PCs. Furthermore, the cross-validation technique was applied for evaluating how the classification results would generalize to independent pork sample sets. A total of 210 partitions (different training and testing sets) were used to obtain the unbiased statistical classification results. The overall classification accuracy reached 84 +/- 1% (mean +/- 95% confidence interval) by 5 hybrid PCs and was 72 +/- 2% when no Gabor filter-based spectral features were used. Thus, a statistically significant improvement was achieved using image texture features. Moreover, the classification results strongly suggested that the texture features along the direction of pi/4 offered more useful information for the differentiation of the four main levels of pork quality. (C) 2010 Elsevier Ltd. All rights reserved.