Retrieving images from a large image dataset using image content as a key is an important issue. In this paper, we present a new content-based image retrieval approach using a wavelet transform and subband image segmentation. For the image retrieval, we first decompose the image using a wavelet transform and adopt a vector quantization(VQ) algorithm to perform automatic segmentation based on image features such as color and texture. The wavelet transform decomposes the image into 4 subbands(LL,LH,HL,HH). Only the LL component is further decomposed until the desired depth is reached. The image segmentation is performed using HSI color and texture features of the low pass subband component image. The VQ provides a transformation from the raw pixel data to a small group of homogeneous classes which are coherent in color and feature sapce. For managing a large image dataset, image compression is usually considered. In that sense the segmentation of a compressed image or subband image is more efficient compared with using an uncompressed image when the compressed image preserves the information needed for the image segmentation task. An important aspect of the system is that using a subband image of the wavelet transform can reduce the size and noise of the image. Thus, we can subsequently reduce the computational burden for the image segmentation. The experimental results of the proposed image retrieval system confirm the feasibility of our approach in retrieving accuracy and in lowering computational cost compared to using the original image.