This paper proposes a number of content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. The goal is to fuse several descriptors in order to improve the performance of several machine-learning classifiers. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A merging fusion combined with an SVM classifier, a back-propagation fusion combined with a K-Nearest Neighbor classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the semantic gap between the low- level descriptors and the high-level semantics of an image. All networks were evaluated using content from the aceMedia Repositoryand more specifically in a beach/urban scenes classification problem.