In recent decades, condition-based maintenance (CBM) is acknowledged a cost-effective and widely used maintenance program for engineering systems. Diagnostics and prognostics are critical components of CBM responsible for offering information about present and future system conditions. These two components are respectively an integrated process covering several aspects that are essential for successful implementations of diagnostics and prognostics. For diagnostics, data to be used should be clean and useful. Data cleaning can provide clean data by removing outliers caused by noise, while feature selection can select useful characteristic features for fault classification. For prognostics, noise may appear in condition indicator values. Using such noisy values may result in unreliable predictions for prognostics. A method is thus demanded to provide predictions without noise effects. Support vector machine (SVM), a machine learning method, is recognized having good generalization ability and an effective tool for classification and prediction needed by diagnostics and prognostics. This book explores the potentials of SVM for addressing above problems in diagnostics and prognostics.