A new method for the study of molecule chemical information organized into three-way data structures (MOLMAP) was recently proposed in literature. Basically, MOLMAP molecular fingerprints are calculated by projecting bond properties of molecules into Kohonen networks and used to generate molecular descriptors for QSAR modeling. Since this technique has never been applied to other kinds of chemical multiway data, in this study classification models were carried out by means of MOLMAP approach on three-way analytical datasets of electronic nose and fluorescence data. For comparing purposes, other classification methods were applied to the same datasets: Discriminant Analysis on the PARAFAC scores and Partial Least Square-Discriminant Analysis (PLS-DA) on the unfolded data. Since the MOLMAP approach provided good results for the analyzed datasets, here, we propose the MOLMAP approach to be used as a general technique for the classification of multiway datasets. Actually, besides the good classification performances, other advantages came out: (a) the MOLMAP scores appeared as effective fingerprints for data characterization; (b) the role and importance of each portion of the multiway data can be analyzed in a comprehensive way; (c) it is possible to understand which variables have greater discriminant power and consequently apply data reduction.