Urban land cover classification using high-resolution imagery is important for many applications where detailed and precise urban land cover products are needed. Machine learning algorithms are currently some of the most commonly used methods for classifying high-resolution imagery due to their impressive capabilities. However, the reliability of the land cover products obtained from the classification of high-resolution urban imageries is dependent upon the accuracy of the Machine Learning (ML) classification algorithm used. The need for an appropriate selection of classifiers for urban land cover classification and their applicable settings necessitates the performance comparison of major ML algorithms used for classification. In this study, we compared the performance of three major Machine Learning (ML) classifier algorithms using a high-resolution image dataset of an urban area. The algorithms are Support Vector Machine (SVM), Naïve Bayes, and Ensemble classifiers. The performance of three model types of SVM classifiers namely Medium Gaussian, Linear, and Quadratic SVM, two model types of Naïve Bayes classifiers namely Gaussian and Kernel Naïve Bayes, and three model types of ensemble classifiers namely Bagged Trees, Subspace Discriminant, and RUSBoosted Trees were compared. Performance evaluation was carried out using Confusion Matrix (CM) and Receiver Operating Curves (ROC) plots. Results obtained from the comparison of the three ML classifier algorithms show that the Subspace Discriminant ensemble classifier had the highest accuracy at 85.1%, closely followed by the Medium Gaussian SVM classifier (84.5%) and Gaussian Naïve Bayes classifier (81.5%). This research provides insights into the selection of classifiers for future urban land cover classification and their applicable settings.