We present an algorithm for detecting multiple rotational symmetries in natural images. Given an image, its gradient magnitude field is computed, and information from the gradients is spread using a diffusion process in the form of a Gradient Vector Flow (GVF) field. We construct a graph whose nodes correspond to pixels in the image, connecting points that are likely to be rotated versions of one another. The n-cycles present in the graph are made to vote for C_n symmetries, their votes being weighted by the errors in transformation between GVF in the neighborhood of the voting points, and the irregularity of the n-sided polygons formed by the voters. The votes are accumulated at the centroids of possible rotational symmetries, generating a confidence map for each order of symmetry. We tested the method with several natural images.