Polymers have become a ubiquitous element of our culture. Therefore, these materials may play an important role in forensic investigations, serving as mute witnesses of occurrences such as car accidents. In this study, the possibilities provided by the likelihood ratio (LR) approach to estimate the evidential value of observed similarities and differences, and to discriminate among NIR spectral data originating from polypropylene automotive parts and household items, were investigated. Since the construction of LR models requires the introduction of only a few variables, the main objective was to reduce the dimensionality of registered spectra, which are characterised by over a thousand variables. The applied strategy was based on compression of NIR signals using discrete wavelet transform (DWT) followed by use of the SELECT algorithm for the selection and decorrelation of the most informative DWT coefficients. Selected features eventually served as an input for LR models. The performance of the developed models was assessed by measuring the rates of false positive and false negative answers as well as by applying an empirical cross entropy approach. Despite relatively small databases of polymeric objects, both univariate and multivariate LR models showed acceptable performances. The latter, however, gave the most satisfactory results, as it enabled successful discrimination of compared samples and delivered the lowest error rates. In addition, in order to verify the potential of NIR spectroscopy, the obtained results were compared with those obtained after application of the proposed tactics to the FTIR data, which is a well-established method in the forensic sphere.