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Edible bird nest (EBN) production is a major industry in South East Asia, motivated by the perception that consumption of this delicacy can help improve the human immune system while slowing down the aging process. However, processing the raw EBN requires a tremendous amount of human effort, making it a very labour-intensive process. The most critical part of this process is the cleaning of the EBN and this is also the most labour intensive and time consuming. Impurities in each sample have to be painstakingly identified and then removed from the rest of the nest. This paper proposed a method to detect the impurities in edible bird nest using K-Means segmentation to help improve the effectiveness of the conventional cleaning process by accurately identifying the impurities of various shapes and sizes. The accuracy of the proposed method has been compared with Fuzzy C-Means.