Support Vector Domain Description (SVDD) is inspired by the Support Vector Classifier. It obtains a sphere shaped decision boundary with minimal volume around a dataset. This data description can be used for novelty or outlier detection. Our approach is always to minimize the volume of the sphere describing the dataset, but following the value of a parameter, which controls its volume and plays a compromise between the outlier’s acceptance and the target’s rejection. Simulation results on seven benchmark datasets have successfully validated the effectiveness of the proposed method.