The use of remote sensing data has become very useful to generate statistical information about society and its environment. In this sense, land use and land cover classification (LULC) are tasks related to determining the cover on the Earth’s surface. In the decision-making process, this kind of information is relevant to handle in the best way how the information about events such as earthquakes or cadastre information can be used. To attend this, a methodology to perform supervised classification based on the combination of statistical, textural, and shape features for the LULC classification problem is proposed. For the experiments, thousands of Landsat and Sentinel images covering all of Mexico and Europe, respectively, were used. Twelve LULC classes were established for Mexico territory, and 10 were established for Europe. This methodology was applied to both datasets and benchmarking with multiple well-known classifiers (random forest, support vector machines, extra trees, and artificial neural network). As a result, the overall accuracy (OA) for Landsat and Sentinel-2 was reached 77.1% and 96.7%, respectively. The performance of the different types of features was compared using Mexico and Europe images. Interesting results were achieved and some conclusions about using traditional and non-traditional features were found in the LULC classification task.