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This paper presents a new application of a well-studied image coding technique, namely block truncation coding (BTC). It is shown that BTC can not only be used for compressing color images, it can also be conveniently used for content-based image retrieval from image databases. From the BTC compressed stream (without performing decoding), we derive two image content description features, one termed the block color co-occurrence matrix (BCCM) and the other block pattern histogram (BPH). We use BCCM and BPH to compute the similarity measures of images for content-based image retrieval applications. Experimental results are presented which demonstrate that BCCM and BPH are comparable to similar state of the art techniques.