The recognition of complex objects from color images is a challenging task, which is considered as a key-step in image analysis. Classical methods usually rely on structural or statistical descriptions of the object content, summarizing different image features such as outer contour, inner structure, or texture and color effects. Recently, a descriptor relying on the spatial relations between regions structuring the objects has been proposed for gray-level images. It integrates in a single homogeneous representation both shape information and relative spatial information about image layers. In this paper, we introduce an extension of this descriptor for color images. Our first contribution is to consider a segmentation algorithm coupled to a clustering strategy to extract the potentially disconnected color layers from the images. Our second contribution relies on the proposition of new strategies for the comparison of these descriptors, based on structural layers alignments and shape matching. This extension enables to recognize structured objects extracted from color images. Results obtained on two datasets of color images suggest that our method is efficient to recognize complex objects where the spatial organization is a discriminative feature.