Radiological chest examinations like chest X-ray play a fundamental role in the fight against the outbreak of COVID-19 pneumonia, caused by the coronavirus strain SARSCov-2. This study aims to investigate classification models to differentiate chest X-ray images of COVID-19-based and typical pneumonia using hand-crafted radiomic features, understanding the distinctive radiographic features of COVID19. A total of 136 segmented chest X-rays from two public databases were used to train and evaluate the classification methods. The PyRadiomics library was used to extract first and second-order statistical texture features in the right (R), left (L), and in superior, middle and bottom lung zones for each lung side. For performing feature selection, data was split in training (80%) and test (20%) sets. Stratified K-folds (K=5) was used within the training dataset for cross-validation. The most relevant radiomic features were selected after measuring validation accuracy and relative feature importance. Support vector machines (SVM), random forest (RF), AdaBoost (AB), and logistic regression (LR) were analyzed as potential classifiers. The AB model was the best discriminant method between features related to COVID-19-based when compared to typical pneumonia, using a model of lung segmentation in six distinct lung zones (AUC =0.98). Our study shows a predominance of radiomic feature selection in the right lung, with a tendency to the upper lung zone.