Image analysis can provide reproducible, objective and accurate results using a non-destructive, inexpensive procedure. The aim of this study was to evaluate the usefulness of the textures calculated from images of the flesh and skin of tomato fruit for cultivar discrimination. The color images of six tomato cultivars were acquired for whole peeled tomatoes and whole tomatoes with skin using a digital camera. The images were converted to color channels R, G, B, L, a, b, X, Y, and Z. The discriminative models were built based on a set of selected textures from all color channels, each color channel, and color space, separately. For images of tomato flesh and skin, the highest average accuracies equal to 92.67% and 94.33%, respectively, were obtained for models combining textures selected from all channels. For color spaces, the correctness reached 92% and 94% for tomato flesh and skin images, respectively. In the case of color channels, cultivar discrimination reached 86.33% for channel a for tomato flesh and 88.67% for channel b for tomato skin. The developed models can be used to evaluate the authenticity of tomato cultivars.