Surface discharges are precursors to flashover. To pre-empt the occurrence of flashover incidents, utility companies need to regularly monitor the condition of line insulators. Recent studies have shown that monitoring of UV signals emitted by surface discharges of insulators is a promising technique. In this work, the UV signals` time and frequency components of a set of contaminated and field-aged insulator under varying contamination levels and degrees of ageing were studied. Experimental result shows that a strong correlation exists between the discharge intensity levels under varying contamination levels and degree of ageing. As the contamination level increases, the discharge level of the insulator samples also intensifies, resulting in the increase of total harmonic distortion and fundamental frequencies. Total harmonic distortion and fundamental frequencies of the UV signals were employed to develop a technique based on artificial neural networks (ANNs) to classify the flashover prediction based on the discharge intensity levels of the insulator samples. The results of the ANN simulation showed 87% accuracy in the performance index. This study illustrates that the UV pulse detection method is a potential tool to monitor insulator surface conditions during service.