Age-related Macular Degeneration (AMD) is the most common cause of blindness in old-age. Early identification of AMD can allow for mitigation (but not cure). One of the fist symptoms of AMD is the presence of fatty deposits, called drusen, on the retina. The presence of drusen may be identified through inspection of retina images. Given the aging global population, the prevalence of AMD is increasing. Many health authorities therefore run screening programmes. The automation, or at least partial automation, of retina image screening is therefore seen as beneficial. This paper describes a Case Based Reasoning (CBR) approach to retina image classification to provide support for AMD screening programmes. In the proposed approach images are represented in the form of spatial-histograms that store both colour and spatial image information. Each retina image is represented using a series of histograms each encapsulated as a time series curve. The Case Base (CB) is populated with a labelled set of such curves. New cases are classified by finding the most similar case (curve) in the CB. Similarity checking is achieved using the Dynamic Time warping (DTW).