The paper investigates the use of support vector machines (SVM) in classifying Matrix-Assisted Laser Desorption Ionisation (MALDI) Time Of Flight (TOF) mass spectra. MALDI-TOF screening is a simple and useful technique for rapidly identifying microorganisms and classifying them into specific subtypes. MALDI-TOF data presents data analysis challenges due to its complexity and inherent data uncertainties. In addition, there are usually large mass ranges within which to identify the spectra and this may pose problems in classification. To deal with this problem, we use Wavelets to select relevant and localized features. We then search for best optimal parameters to choose an SVM kernel and apply the SVM classifier. We compare classification accuracy and dimensionality reduction between the SVM classifier and the SVM classifier with wavelet-based feature extraction. Results show that wavelet-based feature extraction improved classification accuracy by at least 10%, feature reduction by 76% and runtime by over 80%.