In [23] and [24] the authors discussed the effect of signal processing parameters and the influence of measurement conditions. For that reason, various magnetization currents, frequencies and shapes of the excitation signal on the obtained _TF_ spectrograms were considered to achieve the greatest distinguishability between the states of the examined steel. In article [25], the time-frequency analysis along with the J-A model was used to study the effect of stresses. The discussed works showed that the analysis of combined time and frequency (_TF_) domain representations can provide information that is additional to knowledge gathered through traditional approaches. It should be noted, however, that no single feature of the signal expressed in any domain adequately explains the influence of all physical factors affecting a ferromagnetic material. As a result, typical signal analysis approaches may not be enough. Therefore, there is an increasing necessity to apply techniques of searching for complicated rules and relationships within measurement data to build knowledge about the tested material based on multiple features analyses. Two issues are important in this area. The first concerns the search for human-definable features in the measured signals. The second relates to the use of methods based on automatic search for features that do not have straight definition. Currently, methods based on artificial intelligence are very popular in research on finding patterns from data. Various methods, such as machine learning algorithms, shallow and deep artificial neural networks, are used. In the context of MBN, several works [26; 27; 28; 29] using artificial intelligence methods to extract information from signals can be found in the literature. In [26], a Deep Convolutional Neural Network (DCNN) was used to classify various CGO and HGO electrical sheets subjected to a different method of surface engineering. In addition, the proposed deep network model allowed for the evaluation of the angular deviation of a magnetic easy axis from the rolling direction. In [27], the MBN signals expressed in the time domain and autoencoders were used to estimate the magnetocrystalline energy of the examined steel, whereas in [28] a convolutional neural network for the quantitative description of plastic deformation was proposed. Last but not least, paper [29] presented an exploratory data analysis and the application of machine learning methods to describe the surface quality of a material. Additionally, considering the degree of the measured signal processing, the algorithms can be fed with data representing different abstraction levels. In case of the first representation type, the original raw signals are used (data of low abstraction level). The second representation refers to the features vector (data of medium abstraction level) extracted from those measured raw signals (i.e., characteristic or statistical value such as median, mean, maximum or minimum). Under the first representation type, the full information gathered within the measured signal is transferred to the processing algorithms. In the case of the second representation type, the computational effort is much lower, however, it carries a possible loss of information. In the previous paper [26], the authors presented the first type of approach, where the DCNN was utilized. Classification was performed on time-frequency spectrograms of measured MBN signals. This allowed the authors to obtain good classification results. Deep learning makes it possible to search for complex relationships between data, and especially in the case of a very large data set, it shows very good performance. In contrast to the direct feature engineering used in classical ML, the DL procedure is far more complicated, and the results are more challenging to interpret. Therefore, in this paper, to verify the performance and assess the possible advantage of the first type approach to MBN analysis, the second type of measured data representation was used.