This paper presents a survey on vector quantization for image compression. Moreover it provides a means of decomposition of the signal in an approach which takes the improvement of inter and intra band correlation as more lithe partition for higher dimension vector spaces. Thus, the image is compressed without information loss using artificial neural networks (ANN). Since 1988, a growing body of research has examined the use of VQ for the image compression. This paper discusses about vector quantization, its principle and examples, its various techniques and image compression its advantages and applications. Additionally this paper also provides a comparative table in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. In addition the proposed paper also presents a survey on different methods of vector quantization for image compression
