The recent rise of machine learning and deep learning has significantly impacted the field of computer vision, particularly in tasks like object detection, object tracking, and classification. This surge in interest has underscored the critical role of feature extraction as a foundational step in these machine learning pipelines. Our research focuses on applying feature extraction techniques to a cattle lameness dataset. We specifically extract features related to the movement of key points on cattle legs across a sequence of video frames. By analyzing the variations in these points, we aim to identify features that can efficiently differentiate between lame and no lame cattle using popular machine learning algorithms. All four classifiers achieved strong testing accuracy above 75%, with SVM excelling at over 84%.