In this paper, an original framework for grammar-based image understanding handling uncertainty is presented. The method takes as input an over-segmented image, every segment of which has been annotated during a ?rst stage of image classi?cation. Moreover, we assume that for every segment, the output class may be uncertain and represented by a belief function over all the possible classes. Production rules are also supposed to be provided by experts to de?ne the decomposition of a scene into objects, as well as the decomposition of every object into its components. The originality of our framework is to make it possible to deal with uncertainty in the decomposition, which is particularly useful when the relative frequencies of the production rules cannot be estimated properly. As in traditional visual grammar approaches, the goal is to build the ?parse graph? of a test image, which is its hierarchical decomposition from the scene, to objects and parts of objects while taking into account the spatial layout. In this paper, we show that the parse graph of an image can be modelled as an evidential network, and we detail a method to apply a bottom-up inference in this network. A consistency criterion is de?ned for any parse tree, and the search of the optimal interpretation of an image formulated as an optimization problem. The work was validated on real and publicly available urban driving scene data.