Texture classification is a critical task with applications spanning various domains, from facial recognition to cancer detection in medical images. In traditional approaches, the application’s success heavily depends on the feature extraction and classification stages. Over the years, numerous feature extraction methods have been proposed, with non-handcrafted approaches consistently outperforming handcrafted ones. This paper investigates static and dynamic selection techniques of classifiers trained on features extracted from non-handcrafted architectures. The experiments were conducted on two challenging benchmarks widely used for texture classification evaluation: the FMD dataset and the Describable Texture Dataset (DTD). We first evaluated individual features and found that Visual Transformers (ViT) performed exceptionally well compared to other architectures. However, the significantly higher Oracle accuracy for the ensemble of classifiers suggests room for improvement in investigations concerning classifier combination and selection techniques. We observed enhanced performance when combining the top-performing individual classifiers by applying static combination methods such as sum, product, and max rules. Dynamic classifier selection techniques have not yielded improvements in the rates. The best performance was achieved using a static combination of classifiers through sum and product rules. In the FMD database, the F1-score was 93.2% and 81.74% for DTD. The results achieved are among the top three state-of-the-art performances on both datasets.