The recent upsurge in astrophysical research applications of grid technologies, coupled with the increase in temporal acid spatial sky-coverage by dedicated all-sky surveys and on-line data archives, have afforded us the opportunity to develop an automated image reduction and analysis pipeline. Written using Python and Pyraf, the Python implementation of the IRAF package, this has been tailored to act on data from a number of different astronomical instruments. By exploiting inherent parallelisms within the pipeline, we have augmented this project with the ability to be run over a network of computers. Of particular interest to us is an investigation into the latency penalties in running the pipeline within a cluster acid between two clusters. We have used a condensed graph programming model, the Grid middle-ware solution WebCom-G, to realize Grid-implementation. We describe how a re-organisation of such an astronomical image analysis structure can improve operational efficiency and show how such a paradigm can be extended to other applications of image processing. It is intended to use this project as a test bed for eventually running our image processing applications over a grid network of computers, with a view toward possible implementation as part of a virtual observatory infrastructure.