The aim of this study was to employ state-of-art graphics processing unit (GPU) technology and CUDA parallel programming to design and implement a stand-alone pattern recognition (PR) system to discriminate between patients with microischaemic (mIS) and multiple sclerosis (MS) lesions. The dataset comprised MRI image series of 32 patients with mIS and 19 with MS lesions. The probabilistic neural network classifier and 40 textural features, calculated from lesions in the magnetic resonance imaging (MRI) images, were used to design the PR system. The highest classification accuracy was 90.2%, employing six textural features. It took about 135 minutes to design the PR system on a desktop CPU (Intel Core 2 Quad Q9550), using sequential programming, against 250 seconds on the Nvidia 8800GT GPU card, using parallel programming. The proposed PR system may be redesigned on site, when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment.