Identification and classification of different seismo-tectonic events with similar characteristics in a region of interest is one of the most important subjects in seismic hazard studies.In this study,linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul.The vertical components of the digital velocity seismograms are used for seismic events with magnitude(Md)between 1.8 and 3.0 that occurred between 2001 and 2004.Two,time dependent parameters,complexity and S/P peak amplitude ratio are selected as predictands.Linear,quadratic,diaglinear and diagquadratic discriminant functions are investigated.Accuracy of methods with an additional adjusted quadratic models are 96.6%,96.6%,95.5%,96.6%,and 97.6%,respectively with a various misclassified rate for each class.The performances of models are justified with cross validation and resubstitution error.Although all models remarkably well performed,adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179,even better compared to complex methods such as,self organizing method,k-means,Gaussion mixture models that applied to same dataset in literature. Identification and classification of different seismo-tectonic events with similar characteristics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyzes have been applied to classify seismic events in the vicinity of Istanbul The vertical components of the digital velocity seismograms are used for seismic events with magnitude (Md) between 1.8 and 3.0 that occurred between 2001 and 2004. Two time dependent parameters, complexity and S / P peak amplitude ratios are selected as predictands. Linear , quadratic, diaglinear and diagquadratic discriminant functions are investigated. Anccuracy of methods with an additional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6% respectively, with a variety of misclassified rates for each class. models are justified with cross validation and resubstitution error. Though all models remarkably well performed, adjusted quadratic function achieved t he best success rate with just 4 misclassified events out of 179, even better compared to complex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.