Computer vision has been applied to many medical imaging problems. Of the different medical imaging modalities, magnetic resonance (MR) imaging is a powerful and widely used technique for medical diagnosis and is attracting much medical image processing research effort. One use of MR imaging is the detection of benign tumours, called acoustic neuromas, which occur in the auditory canal. At present, these tumours are identified manually from MR images (slices) of the head, a task which is both costly and tedious. Here, a method for automating the detection and localisation of acoustic neuromas from head MR images, using computer vision, is described which comprises of three phases: a) a data driven initial segmentation; b) classification at the pixel level, using a neural network, to identify pixels from acoustic neuromas; c) fusion of the segmentation and the pixel level classification to identify segmented regions likely to belong to an acoustic neuroma. These three phases are discussed and the results of each phase presented. Current and future work is then outlined. The MR images of the head used are 256 × 256 pixels in size, grey scale (8 bits per pixel) images.