Introduction In this era of ‘omics’ technology in natural products studies, the complementary aspects of mass spectrometry (MS)‐ and nuclear magnetic resonance (NMR)‐based techniques must be taken into consideration. The advantages of using both analytical platforms are reflected in a higher confidence of results especially when using replicated samples where correlation approaches can be used to statistically link results from MS to NMR. Objectives Demonstrate the use of Statistical Total Correlation (STOCSY) for linking results from MS and NMR data to reach higher confidence in compound identification. Methodology Essential oil samples of Melaleuca alternifolia and M. rhaphiophylla (Myrtaceae) were used as test objects. Aliquots of 10 samples were collected for GC–MS and NMR data acquisition [proton (1H)‐NMR, and carbon‐13 (13C)‐NMR as well as two‐dimensional (2D) heteronuclear single quantum correlation (HSQC), heteronuclear multiple‐bond correlation (HMBC), and HSQC‐total correlated spectroscopy (TOCSY) NMR]. The processed data was imported to Matlab where STOCSY was applied. Results STOCSY calculations led to the confirmation of the four main constituents of the sample‐set. The identification of each was accomplished using; MS spectra, retention time comparison, 13C‐NMR data, and scalar correlations of the 2D NMR spectra. Conclusion This study provides a pipeline for high confidence in compound identification using a set of essential oils samples as test objects for demonstration.