The analysis conducted in Slemmestad offers valuable insights into the current status of discarded textiles from various sources in Norway. The TPR picking analysis results provide us with information regarding the composition of fibres, garment age, country of production, and the company of the discarded textiles. This data was taken from the labels present on the products. Over a period of 13 days, we successfully managed to analyze a total of 3024 items using the picking analysis method. Interestingly, we also acquired data from 708 individual brands. And as many as 2564 clothing items had the brand present either on the care label or a logo visible on the clothing. While identifying the brand wasn’t problematic, determining the production year proved to be challenging. Different companies and brands had a diverse labelling system, only 95 items out of 3024 had a clear visible production year code labelled "Production Year: xxxx". Most brands utilized different coding systems on their labels, which could be traced back within each company’s individual system. The level of cooperation from contacted companies varied; some brands provided us with the requested information, while others were less cooperative. The progress in the TPR method time efficiency showcases the potential for applying picking analysis and the time it might require. In our TPR approach, we utilized several input parameters and managed to analyze an average of 234 items per day, equivalent to approximately 79 kg. On the other hand, the fast TPR method employed fewer input parameters while still capturing details for each item. With this approach, the average number of items examined within a 30-minute 3 Method for picking analyses of textiles REdu Wasted Textiles Project span is 29, equal to 7.6 kg per person. The time used to analyze was affected by the different textiles analyzed, whereas multi-layer and complicated multi-fibre items were time-consuming. In assessing the potential of machine learning in textile waste management, the results are promising but not without challenges. The first model focusing on textile type classification achieved significant accuracy, with an accuracy of 82.25 %, emphasizing the practicality of using automation for sorting textiles. However, the usability classification model highlighted the need for comprehensive and quality data inputs to predict an item’s reuse potential. While machine learning presents great promise in enhancing sorting efficiency, determining reusability, and promoting fibre-to-fibre recycling, its successful deployment hinges on several factors. These include the expansion and quality improvement of datasets, the integration of advanced sensing technologies, and a broader assessment of environmental, economic, and social impacts. Ethical considerations are paramount, especially in ensuring that machine learning models operate effectively and ethically. By partnering with EPR schemes, feedback loops between producers and waste management can be optimized, steering production toward sustainability. As we forge ahead, refining data collection methods, embracing advanced technology, expanding impact assessments, and fostering partnerships will be pivotal in harnessing the full potential of machine learning in textile waste management.The data gathered from the examined items highlights the untapped potential of reusing materials in the textile sector. Assessing the level of wear and tear in clothing was relatively easy, using factors like pilling, stains, discolouration, damaged zippers, missing buttons, holes, and general signs of use. A majority of the items showcased good usability scores, where 42% of the textiles scored a condition of 4, which means the condition of the garment is considered as good. Also, a considerable portion of the analyzed items appeared almost new and were therefore discarded prematurely. 21 items were found with their price tags still attached. While some items displayed minor damages, many of these can be fixed effortlessly at home, pointing towards the value of imparting basic repair skills to consumers. The data obtained from this analysis holds significant importance in comprehending the possibilities of automated sorting, material reuse, and recycling. It provides a foundation for introducing strategic methods like extended producer responsibility (EPR) and policies focused on waste prevention. Hopefully, the TPR picking analysis can be used to assess eco-modulated environmental fees in a producer responsibility (EPR) scheme, aiming to ultimately decrease excessive textile production in the fast fashion sector.