In this paper commonly-used overhead line insulators are experimentally studied to propose an intelligent diagnosis method (IDM) to correctly identify the insulator health condition in real-time. The proposed IDM is developed based on three diagnostic indicators, third to fifth harmonic ratio of the insulator’s leakage current (LC), cosine of the phase angle of the LC fundamental component, and the ratio of the maximum electric field stress to flashover electric field of the insulator. The proposed diagnostic approach can identify the normal, abnormal and critical conditions of an insulator based on the above-mentioned indicators. Leakage current and flashover voltage are experimentally recorded for the studied insulators under various health conditions. Then, recorded data are analyzed to calculate the proposed indicators corresponding to each insulator state. Measured and calculated data are used to intelligently quantify threshold limits of each indicator based on visualization algorithm. In this algorithm, different classifiers are trained with experimental data and the classifier with the highest precision is employed to determine reference values for each health condition of the insulator