The colorimetric characterization of a color image sensor was developed and modeled using a convolutional neural network (CNN), which is an innovative approach. Color image sensors can be incorporated into compact devices to detect the color of objects under a wide range of light sources and brightness. They should be colorimetrically characterized to be suitable for smart industrial colorimeters or light detection. Furthermore, color image sensors can be incorporated into machine vision systems for various industrial applications. However, the red, green, and blue (RGB) signals generated by a color image sensor are device-dependent, which means that different image sensors make different RGB spectrum responses under the same conditions. Moreover, the signals are not colorimetric; that is, output RGB signals are not directly coherent in terms of device-independent tristimulus values, such as CIE XYZ or CIELAB. In this study, the colorimetric mapping of RGB signals and CIELAB tristimulus values by CNN modeling was proposed. After digitalizing an RGB image sensor, characterizing RGB colors in the CIE color space, and CNN modeling for precise accuracy, the colorimetric characterization of color image sensors based on CNN modeling was proved to be superior to that based on 3 × N polynomial regression. ΔE*ab was less than 0.5.