Memory colours refer to the colour of specific image classes that have the essential attribute of being perceived in a consistent manner by human observers. In colour correction or rendering tasks, this consistency implies that they have to be faithfully reproduced; their importance, in that respect, is greater than other regions in an image. Before these regions can be properly addressed, one must in general detect them. There are various schemes and attributes to do so, but the preferred method remains to segment the images into meaningful regions, a task for which many algorithms exist. Memory colours’ regions are not, however, similar in their attributes. Significant variations in shape, size, and texture do exist. As such, it is unclear whether a single algorithm is the most adapted for all of these classes. In this work, we concern ourselves with three memory colours: blue sky, green vegetation, and skin tones. Using a large database of real-world images, we (randomly) select and manually segment 900 images that contain one of the three memory colours. The same images are then automatically segmented with four classical algorithms. Using class-specific eigenregions, we are able to provide insights into the underlying structures of the considered classes and class-specific features that can be used to improve classification’s accuracy. Finally, we propose a distance measure that effectively results in determining how well is an algorithm is adapted to segment a given class.