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This tomato maturity estimator is developed to conduct tomato color grading using machine vision to replace human labor. Existing machine has not been widely applied in Malaysia since the cost is too expensive. The major problem in tomato color grading by human vision was due to the subjectivity of human vision and error prone by visual stress and tiredness. Therefore, this system is carried out to judge the tomato maturity based on their color and to estimate the expiry date of tomato by their color. Evolutionary methodology was implemented in this system design by using several image processing techniques including image acquisition, image enhancement and feature extraction. Fifty sample data of tomatoes were collected during image acquisition phase in the format of RGB color image. The quality of the collected images were being improved in the image enhancement phase; mainly converting to color space format (L*a*b*), filtering and threshold process. In the feature extraction phase, value of red-green is being extracted. The values are then being used as information for determining the percentage of tomato maturity and to estimate expiry date of tomato. According to the testing results, this system has met its objectives whereby 90.00% of the tomato tested has not rotten yet. This indicates that the judgment of tomato maturity and the estimation of tomato's expiry date were accurate in this project.