The garment industry is one of the most pollutant and prominent producers of waste on the planet. Only a low percentage of the used textile resources are reused to manufacture new clothes, and second-hand markets are becoming more saturated, causing that clothing that can still be worn to be discarded. A crucial alternative for the garments industry to reduce the environmental impact is closed-loop recycling; however, there are still challenges, such as the automation of sorting processes, that need to be tackled to enable circularity. This thesis is developed within the cooperation framework of the Freie Universität Berlin, the Technische Universität Berlin, and the circular fashion company to support CRTX. CRTX is a collaborative project that researches solutions to automate the sorting of used garments for high-quality purposes and to support human sorters to achieve a fine-grained classification. During the sorting process, previously unseen garment categories may appear that need to be classified. This work explores a meta-learning approach, which recognizes new classes from only a few labeled examples of each class, as an alternative to classify such categories. Results show that these methods are scalable to new classes and robust to imbalanced datasets, closer to real-world conditions. For the experimentation, a Machine Learning pipeline was built using state-of-the-art tools, which also contributes to the objective of an eventual system deployment for production-level serving.