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This paper presents a proposed approach for distillation column malfunction identification using Higher Order Statistics (HOS). Gamma ray scanning techniques have been used for examining the inner details of a distillation column. In the proposed method, the signals are firstly divided into frames; each frame contains only the signal of one column tray. Secondly, HOS are estimated for these frame signals. Then features are extracted from the HOS estimate. Finally, features are used for training and testing of Artificial Neural Network (ANN) to identify the distillation column malfunctions. The simulation results show that the HOS can be used efficiently for the distillation column malfunction identification especially at high noisy scanning conditions.