Recent estimations considered that up to 40% of oil spills in the US governmental jurisdiction remained unidentified. Many circumstances may be behind this striking figure, one of them the difficulties in the analytical characterization of the test aliquots and the subsequent extraction of information. In this paper a simple approach to handle complex datasets (organized as oils x weathering aliquots x analytical variables) is proposed. It considers self-organizing maps to gather information about: (i) the samples, and, so, whether the oils can be differentiated; (ii) the weathering of the oils; and (iii) the analytical variables (in this case, mid-IR spectral indexes) that characterize each oil and weathering. It was verified that the oils can be differentiated, even after their weathering. Besides, a quite good ordering of the aliquots as a function of time was revealed. Considering the two different arrangements that were studied (I x J x K or J x I x K) the results obtained are totally similar. The major difference was that when the (I x J x K) arrangement; i.e., oils x weathering aliquots x IR indexes, was considered, the final PCA (made with the MOLMAP-scores) discriminated immediately between the different products. Considering the (J x I x K) ordering; i.e., weathering aliquots x oils x IR indexes, the PCA emphasized the different stages of weathering. (C) 2015 Elsevier Ltd. All rights reserved.