  A detailed case study of $\gamma$-hadron segregation for a ground based
atmospheric Cherenkov telescope is presented. We have evaluated and compared
various supervised machine learning methods such as the Random Forest method,
Artificial Neural Network, Linear Discriminant method, Naive Bayes
Classifiers,Support Vector Machines as well as the conventional dynamic
supercut method by simulating triggering events with the Monte Carlo method and
applied the results to a Cherenkov telescope. It is demonstrated that the
Random Forest method is the most sensitive machine learning method for
$\gamma$-hadron segregation.
