Wear debris provides important information about the machine condition that can be used to prevent the loss of expensive machinery. This information is crucial as it portrays the condition of the machines and can be used to predict early failure of the machinery that can prevent a major loss to the industry. Wear debris or particles produced in different parts of machine vary in shape, size, color, and texture. These characteristic features can be utilized to identify the type of wear debris. Human experts are extremely efficient in recognizing such objects; however, wear judgments are occasionally based on their specific perceptions. The goal is to look beyond the personal opinions and bring consistency in judging and recognizing wear particles. Keeping in view the above findings, this study focuses on the identification of wear particles by using shape-based features only. Different shape features, which include the Histogram of Oriented Gradients (HOG), Rotation Scale Translation (RST) invariant features, solidity, aspect ratio, circularity, and Euler number, are extracted and used to train three classification models of Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and discriminant analysis (DA) classifier. The performance of the classifiers are compared with each another and classification of debris based on shape features is analyzed.