This paper addresses the on going work of the application of Machine Learning on Static Security Assessment of Power Systems. Several techniques, which have been applied for static Security Assessment .A Decision Tree types comparison for the purpose of static security assessment classification is discussed and the comparison results from these methods on operating point are presented. Decision Tree examines whether the power system is secured under steady-state operating conditions.DT gauges the bus voltages and the line flow conditions. Using minimum number of cases from the available large number of contingencies in terms of their impact on the system security is the methodology that has been developed. Newton Raphson load flow analysis method is used for training and test data. The input variables to the network are loadings of the lines and the voltage magnitude of the load buses. The algorithms are initially tested on the 5 IEEE bus systems.The results obtained indicate that DT method is comparable in accuracy and computational time to the Newton Raphson load flow method