<p>Hand gesture recognition (HGR) is increasingly adopted in real-world applications, yet selecting the most suitable model–hyperparameter configuration remains a time-consuming and opaque process. This work introduces a systematic evaluation framework for static HGR that combines transfer learning (TL) with a fuzzy logic expert system to provide a reproducible and transparent ranking of candidate models. We benchmark eight widely used CNN backbones across 64 fine-tuned configurations, analyzing their performance in terms of accuracy, recall, and F1-score, while explicitly documenting the fuzzy inference rules used to integrate these metrics. Experiments on 3-class and 5-class subsets of the Tiny Hand Gesture dataset consistently highlight the robustness of classical architectures such as VGG16 and ResNet50, with the best configuration achieving an F1-score of 0.995. Beyond accuracy, we extend the analysis to computational efficiency, showing that deeper or next-generation networks often incur higher training time and memory costs without yielding performance gains. Finally, we report an exploratory hybrid quantum–classical experiment based on ResNet50 with a variational quantum layer, which improved test accuracy from 0.876 to 0.94, at the expense of extremely high training costs. Overall, our contribution is threefold: (i) a reproducible TL evaluation pipeline with fuzzy ranking, (ii) an evidence-based comparison of classical versus modern CNN architectures in static HGR, and (iii) an initial exploration of quantum machine learning integration for gesture classification.</p>