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Detection and correct identification of gasoline types during Arson and Fuel Spill Investigation are very important in forensic science. As the number of arson and spillage becomes a common place, it becomes more important to have an accurate means of detecting and classifying gasoline found at such sites of incidence. However, currently only a very few number of classification models have been explored in this germane field of forensic science, particularly as relates to gasoline identification. In this work, we developed extreme learning machines (ELM) based identification model for identifying gasoline types. The model was constructed using gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Prediction accuracy of the model was evaluated and compared with earlier used methods on the same datasets. Empirical results from simulation showed that the proposed ELM based model achieved better performance compared to other earlier implemented techniques.