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The method of identifying the type of fabric used in CV. XYZ is still using the manual method so that it increases the processing time. In Industry 4.0, computer vision is becoming a popular application for automatic identification of fabrics. This study designed a computer vision-based fabric classification model that uses a convolutional neural network algorithm. This model aims to classify types of polyester and blended fabrics. There are 5 convolutional neural network models implemented in this study, namely the prefix model, MobileNetV2, VGG- 16, ResNet50, and Xception. In this study a web-based application was also designed using the Flask framework to apply the classification model. Based on the results of the research conducted, it was found that the Xception model has an average training accuracy of above 90% and a success rate of above 90% in classifying fabric types. This shows that this model has a good performance in identifying the type of polyester and blended fabrics.