In chemometrics, a spectrum is usually represented in a form of numeric vector for further processing. The same approach has been used also for spectral data treatment by convolutional neural networks (CNN), initially purposed, however, for image processing. Analyzing spectral data as a two-dimensional picture rather than a one-dimensional vector potentially can improve the accuracy of regression and classification models. The purpose of this work was to test this assumption. As a first case study we have chosen one of the most difficult types of spectra to interpret – the Mössbauer spectral data. We explored the potential of 2D-CNN to predict Mössbauer spectral parameters numerically using image (.bmp) files with spectra. As a second case study, we address another challenging task – classification of biological tissues using their near-infrared spectra. In both cases, we compared the performance of CNN for two types of input data: spectra as pictures and as numeric vectors. It was found that in case of Mossbauer spectra, the use of 2D-CNN provides for better prediction of spectral parameters, while for NIR spectra of biological tissues the use of the traditional PLS-DA method turned out to be better for classification compared to the CNN approach. © 2024 Elsevier B.V.