Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e.g., documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors. A feature vector is a vector that represents a set of features, and are usually high-dimensional data. The performance of conventional multidimensional data structures (e.g., R-tree family K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R∗-tree is the most successful variant of the R-tree. We propose a SOM-based R∗-tree as a new indexing method for high-dimensional feature vectors. The SOM-based R∗-tree combines SOM and R∗-tree to achieve search performance more scalable to high dimensionalities. Self-organizing maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. We experimentally compare the retrieval time cost of a SOM-based R∗-tree with that of an SOM and an R∗-tree using color feature vectors extracted from 40,000 images.