Changing in lifestyle, the perceived impurity of tap water, and the perceived cleanliness of bottled water can be considered as the cause for the increasing demand of plastic water bottle users. Bottling companies strive for quality when producing bottled water. Because, different defects occurred on the bottles in the production process. So, it’s important to check defects during the water bottling process. It is a difficult task for human beings to detect the defects from a huge number of water bottles in the production line during the inspection process. Therefore, various researchers are motivated to develop defect detection models based on image processing. However, as we have investigated from the previous research they are motivated to detect different defects between glass and plastic bottles. But the previous researchers do not consider different shape of the bottles. In this study, we have performed a plastic water bottle defect detection using a machine learning approach. We have examined defects such as (broken cap defect, no cape defect, scratch cap defect, shape defect, and water level defect) and non-defects of the bottle. To achieve the objective of the study, plastic water bottle images with different shapes and sizes are considered from Choise, Eleleta, and Kefita plastic water bottling PLCs. A total number of 9,000 plastic water bottle images were taken by using Samsung Galaxy S7. After getting the dataset we have done preprocessing tasks like resizing, noise reduction, grayscale conversion, histogram equalization, and segmentation. For feature extraction, we have used Histogram Oriented Gradients (HOG), Convolutional Neural Network (CNN), and a combination of HOG and CNN. For classification, we have used end-to-end CNN, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The proposed hybrid CNN and HOG feature trained with CNN, SVM, and KNN classifiers achieved the highest accuracy obtained in this work. From the experimental results, our end-to-end CNN model achieved 94.33% accuracy, using SVM classifier with HOG, CNN, and combined (i.e. HOG and CNN) features is 82.27%, 94%, and 99% respectively. And also we have got an accuracy of using KNN classifier with HOG, CNN, and combined (i.e. HOG and CNN) features is 81%, 82%, and 97% respectively. Therefore, the combined feature of HOG and CNN with SVM classifier has outperformed the better accuracy for detecting defects in plastic water bottles. Keywords: Histogram Oriented Gradient, Convolutional Neural Network, Defect Detection, Feature extraction, Support Vector Machine, and K-Nearest Neighbor.