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A new class of neuro-fuzzy systems, based on so-called Weighted Neural Networks (WNN), is introduced and used for unsupervised fuzzy clustering and image segmentation. Incremental and fixed (or grid-partitioned) Weighted Neural Networks are presented and used for this purpose. The WNN algorithm (incremental or grid-partitioned) produces a net, of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in the input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of resulting clusters is determined by this procedure. Experiments confirm the usefulness and efficiency of the proposed neuro-fuzzy systems for image segmentation and, in general, for clustering multi- and high-dimensional data.