A medical image processing system for the recognition of CT brain scan images has been developed. This system was successfully tested on its ability to correctly classify the human brain scans as 'normal', 'haemmoraged', and 'lacunar infarcted'. The imaging system composed of a variation of Laplacian of the Gaussian (LoG) edge detector, a chain encoder, the Hough transform, and a backprop neural network. The edge detector output was fed into the chain coder which formed meaningful segments or groupings of some important features present in the image. These features were further processed by the Hough transform to identify any analytical shapes in these features or clusters. All this information was processed so that with minimal user input the imaging system determined the size and the shape of some feature such as the third ventricle in a brain scan. The neural network was presented with a seven vector input in the case of brain scans which resulted in a 3 bit output. This output was interpreted as a probability whether the given brain scan was 'normal', haemmoraged', or lacunar infarcted'. The backup correctly classified CT brain scans in approximately 95% of the test cases for normal images, and 85% of the cases for hemorrhaged and lacunar infarcted images.