Abstract: The challenging for buyers around the globe to identify good quality of Durian. For several kinds of Durian, it may be difficult for buyers to determine the Durian quality by appearance. The ability to select only good quality Durian without cutting or cleaving is useful because buyers will not waste money ordering undesirable Durian. This study proposes a nondestructive technique to determine the stages of maturity of durian fruits. The presented methodology utilizes the concept of pattern matching. We used the local knocking equipment to knock the durian for knocked-sound. After that the knocked-sound was analyzed and generated to Mel-Frequency Cepstral Coefficients (MFCCs) that is used to train data for the classifier. Feed-forward neural network was used for the classifier and can effectively classification the stages of maturity of durian fruits with accuracy rate >82%.