A multichannel hyperspectral imaging probe with 30 optic fibers covering the wavelength range of 550～1 650 nm and the light source-detector distances of 1.5～36 mm was recently developed for property and quality assessment of food products. Spatially resolved spectra were acquired using the new developed multichannel probe for 600 “Sun Bright” tomato fruit, which were grouped into six maturity grades (i.e., green, breaker, turning, pink, light red, red), based on their internal color distributions. Partial least squares discriminant analysis (PLSDA) and support vector machine discriminant analysis (SVMDA) models for the 15 spatially resolved spectra were developed and compared to determine the optimal models for classification of tomato maturity. The results showed that for PLSDA models, SR spectra 15 gave the best classification results with the accuracy of 81.3%, while for SVMDA models, SR spectra 10 had the best accuracy of 86.3%. Overall, SVMDA models will provide better performance than PLSDA models for tomato maturity classification. Moreover, spatially resolved spectra with larger source-detector distances could offer better classification results, which suggests that spatially resolved spectra has potential for measuring fruit and vegetables quality.