Urban sprawl generally refers to the amount of concrete jungle in a given area. In the present context, we consider a metropolitan area of Bangalore. The area has grown tremendously in the past few years. To find out how much of the area is occupied by built‐up areas, we consider the remotely sensed images of the Bangalore Urban District. Each material on the earth's surface reflects a different wavelength, which is captured by the sensors mounted on a satellite. In short, the spectral signatures are the distinguishing features used by the machine learning algorithm, for classifying the land cover classes. In this study, we compare and contrast two types on machine learning algorithms, namely, parametric and non‐parametric with respect to the land cover classification of remotely sensed images. Maximum likelihood classifiers, which are parametric in nature, are 82.5% accurate for the given study area, whereas the k‐nearest neighbor classifiers give a better accuracy of 85.9%.