The quality of 16 types of pork (PK) and poultry (PL) cold meats was evaluated by digital image analysis. Images were acquired in a flatbed scanner. The dry matter, protein, fat, ash and collagen content of the analyzed products was determined, and more than 2800 image texture variables from 12 color channels (RGB, Lab*, XYZ, S, V, U) were measured. The results were processed statistically by one-way ANOVA, correlation analysis, discriminant analysis and canonical analysis. Canonical analysis was performed to determine correlations between the chemical composition and image textures of cold meats. The developed statistical model discriminated cold meats with 89%–100% accuracy, subject to product type. The coefficients of correlation between chemical composition and image texture parameters were determined in the range of 0.70–0.92.