  We have studied the application of different classification algorithms in the
analysis of simulated high energy physics data. Whereas Neural Network
algorithms have become a standard tool for data analysis, the performance of
other classifiers such as Support Vector Machines has not yet been tested in
this environment. We chose two different problems to compare the performance of
a Support Vector Machine and a Neural Net trained with back-propagation:
tagging events of the type e+e- -> ccbar and the identification of muons
produced in multihadronic e+e- annihilation events.
