This paper describes a rotation, translation, and scaling (RTS) invariant color image indexing technique for imaging database systems. The features used for image indexing are color based, which are extracted, using the principal component analysis, Hotelling transform, and moment invariants. This synthesized feature extraction technique is devised to be computationally efficient for on-line fast image storage and retrieval, using color information. Since the database indexing relies on the use of average (mean) color vector, and seven moment invariants of an image, the index storage requirement of the method is only a ten- dimensional (10-D) vector. This index storage efficiency is very desirable for many imaging database applications. A new similarity measure is also proposed, based on the Tanimoto measure of recognizing similar patterns for fast image retrieval in large database systems. The underlying similarity measure is computationally effective, since the vector inner product is the only operation needed for its computation. Four databases are used in the computer simulation of the algorithm, to demonstrate the RTS property of the image retrieval. It is determined, experimentally, that the proposed method is not affected by substantial changes in the database images, due to rotation, translation, and scaling. This is attributed to the fact that the moment features used for retrieval are not predefined set. They are, rather, obtained directly from the images submitted for recording or searching. This makes the algorithm very robust and attractive for many applications of the image storage and retrieval systems.