Document type classification is essential for effective information retrieval and management within archival systems, particularly in low-resource languages like Afrikaans. This study examines the feasibility of utilising multilingual transformer-based language models for document classification within a South African archival context. We followed a basic linguistic approach to prepare Afrikaans text documents for classification into six categories: academic papers, media reports, books, interviews, book reviews, and theses or dissertations. We compare fine-tuned transformer models, hybrid models combining traditional classifiers with contextual embeddings, and a baseline SVM (TF-IDF) classifier, using stratified 5-fold cross-validation and a hard voting ensemble for robust evaluation. Our findings reveal that the SERENGETI transformer-based model outperformed other multilingual models, achieving a weighted F1 score of 0.964, while hybrid approaches performed competitively. However, the baseline SVM (TF-IDF) model outperformed all transformer and hybrid models, with a weighted F1 score of 0.978. This research demonstrates the potential and current limitations of neural language models and hybrid strategies for enhancing document classification in Afrikaans archival systems. If implemented, the classifier can improve indexing efforts and reduce pressure on archival personnel who handle over 5,000 new items annually.