Poultry meat color is an important quality attribute for the rapid detection of "pale poultry syndrome", as it is affected by conditions of animal welfare during pre-mortem period. The meat processing industry demands a fast and non-contact method for fast and accurate meat color assessment. In the present study, computer vision was tested as a potential tool to predict color measurements in contrast to CIELab attributes of chicken breast (pectoralis major ), compared to analytical reference measurements. The proposed approach using computer vision was successful in avoiding pixels with little information (specular reflection). High correlation coefficients obtained between computer vision and colorimeter validate the approach for measure L* color component. Results for correlation and determination coefficients were respectively R = 0.93 and R2 = 0.89 for L*. In addition, our framework reach a correlation of r = 0.88 and R2 = 0.77 for a*, and a correlation of r = 0.88 and coefficient of determination R2 = 0.77 for b* component. Results suggest that computer vision methods based on a RGB device can become useful tool for fast quality assessment of chicken meat in large-scale processing plants.