¹In this paper, we propose a technique to visualize a multitude of images on a 2-D screen based on their visual features (color, texture, structure, etc.). The resulting layout will automatically display the mutual similarities of the viewed images. Furthermore, audio features, semantic features, or any combination of the above can be used in such a visualization. The original high dimensional feature space is projected on the 2-D screen based on Principle Component Analysis (PCA). PCA has the desired property of being simple, fast and unique (i.e. repeatable) and the only linear transformation that achieves maximum distance preservation in projecting to lower dimensions. Furthermore, we have developed a novel technique for solving the problem of overlapping (obscured) images shown in the proposed 2-D display. Given the original PCA-based visualization, a constrained nonlinear optimization strategy is used to adjust the position and size of the images in order to minimize overlap (maximize visibility) while maintaining fidelity to the original positions which are indicative of mutual similarities. A significantly improved visualization of large image sets is achieved when the proposed technique is applied.