One of the goals of NASA funded project at IBM T. J. Watson Research Center was to build an index for similarity searching satellite images, which were characterized by high-dimensional feature image texture vectors. Reviewed is our effort on data clustering, dimensionality reduction via Singular Value Decomposition - SVD and indexing to build a smaller index and more efficient k-Nearest Neighbor - k-NN query processing for similarity search. k-NN queries based on scanning of the feature vectors of all images is obviously too costly for ever-increasing number of images. The ubiquitous multidimensional R-tree index and its extensions were not an option given their limited scalability dimension-wise. The cost of processing k-NN queries was further reduced by building memory resident Ordered Partition indices on dimensionality reduced clusters. Further research in a university setting included the following: (1) Clustered SVD was extended to yield exact k-NN queries by issuing appropriate less costly range queries, (2) Stepwise Dimensionality Increasing - SDI index outperformed other known indices, (3) selection of optimal number of dimensions to reduce query processing cost, (4) two methods to make the OP-trees persistent and loadable as a single file access.