Research on the non-stationary nature of road vehicle vibrations (RVV) led to advances in simulating such processes. Contemporary methods introduced for the analysis of RVV primarily aimed at partitioning the signal in the time- or time − frequency domain, providing differing segments of a signal. However, a degree of dissimilarity, or conversely similarity, is still challenging to find. Hereunder we argue that in some cases, merely a statement of dissimilarity between neighbouring segments within a signal might be well-enough, though from a broader perspective, the assessment of the similarity of discrete Fourier transforms (DFT) may be the next practical step forward. For this reason, the current paper presents the hierarchical clustering of elements of the short-time Fourier transform (STFT) plane from an RVV measurement; secondly, it introduces a clustering validation metric to arrive at an optimum distance metric and a threshold to use in binary hierarchical clusters.