Recently, several image indexing techniques have been reported in the literature. However, these techniques require a large amount of off-line processing, additional storage space and may not be applicable to images stored in compressed form. In this paper, we propose two efficient techniques based on vector quantization (VQ) for image indexing. In VQ, the image to be compressed is decomposed into L-dimensional vectors. Each vector is mapped onto one of a finite set (codebook) of reproduction vectors (codewords). The labels of the codewords are used to represent the image. In the first technique, for each codeword in the codebook, a histogram is generated and stored along with the codeword. We note that the superposition of the histograms of the codewords, which are used to represent an image, is a close approximation of the histogram of the image. This histogram is used as an index to store and retrieve the image. In the second technique, the histogram of the labels of an image is used as an in index to access the image. The proposed techniques provide fast access to the images in the database, have lower storage requirements and combine image compression with image indexing. Simulation results confirm the gains of the proposed techniques in comparison with other techniques reported in the literature.