Elementary particle physics experiments, searching for very rare processes require the efficientanalysis and selection algorithms able to separate signal from the overwhelming background. Inthe last ten years a number of powerful kernel-based learning machines, like Support VectorMachines (SVM), have been developed. SVM approach to signal and background separation isbased on building a separating hyperplane defined by the support vectors. The margin betweenthem and the hyperplane is maximized. The extensions to a non-linear separation are performed bymapping the input vectors into a high dimensional space, in which data can be linearly separated. The use of kernel functions allows us to perform computations in a high dimension feature spacewithout explicitly knowing a mapping function. We have implemented an SVM algorithm and integrated it with the CERN ROOT package, which is currently a standard analysis tool used by elementary particle physicists. We also used theimplemented SVM package to identify hadronic decays of τ leptons in the ATLAS experiment atLHC accelerator. The performance of the method is compared to the likelihood estimator, whichdoes not take into account correlations between variables. The use of SVM significantly reducesthe number of background events.