Determining and continuously monitoring the quality parameters (proximate and calorific value analysis) of coal is crucial for mining, power generation, and trading purposes. Although proximate and calorific analyses are compulsory for monitoring, on-line methods continuously determine these parameters using expensive, detached equipment. Few studies predict the coal quality parameters using image analysis of the optical changes of coal surfaces. This study aimed to test the hypothesis that the surface color of coal samples might be an indicator for coal quality parameters. A novel method is proposed to estimate the ash content, fixed carbon, and gross calorific value of the given coals by analyzing the colored images of coal samples. Thirty-five coal samples from seven lower-rank coalfields in Turkey were examined through proximate and calorific analyses. For this preliminary study, they were photographed to obtain digital images, processed to calculate each pixel’s RGB codes as 8-bit values and normalized RGB codes, i.e., chromaticity values. The analyses showed that blue and red chromaticity values are highly correlated with ash content, fixed carbon content, and calorific values.