Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases associated with supervised classification methods. In this study, we follow supervised classification scheme under classifiers, the support vector classifier (SVC), and multilayer perceptrons (MLP) to provide an opportunity for directly assessing the feasibility of different classifiers. Before choosing classifier, we evaluate extracted seismic attributes using forward feature selection (FFS) and backward feature selection (BFS) methods for logical SFA. The analyses are examined with data from an oil field in Iran, and the results are discussed in detail. The numerical relative errors associated with these two classifiers as a proxy for the robustness of SFA confirm reliable interpretations. The higher performance of SVC comparing to MLP classifier for SFA is proved in two validation steps. The results also demonstrate the power and flexibility of SVC compared with MLP for SFA.