This work introduces a fuzzy rule-based system operating as a selector of color constancy algorithms for the enhancement of dark images. In accordance with the actual content of an image, the system selects among three color constancy algorithms, the White-Patch, the Gray-World and the Gray-Edge. These algorithms have been considered because of their accurate remotion of the illuminant, besides showing an outstanding color enhancement on images. The design of the rule-based system is not a trivial task because several features are involved in the selection. Our proposal consists in a fuzzy system, modeling the decision process through simple rules. This approach can handle large amounts of information and is tolerant to ambiguity, while addressing the problem of dark image enhancement. The methodology consists in two main stages. Firstly, a training protocol determines the fuzzy rules, according to features computed from a subset of training images taken from the SFU Laboratory dataset. We choose carefully twelve image features for the formulation of the rules: seven color features, three texture descriptors, and two lighting-content descriptors. In the rules, the fuzzy sets are modeled using Gaussian membership functions. Secondly, experiments are carried out using Mamdani and Larsen fuzzy inferences. For a test image, a color constancy algorithm is selected according to the inference process and the rules previously defined. The results show that our method attains a high rate of correct selection of the most well-suited algorithm for the particular scene.