Urbanization is causing people to live close to chicken houses. Their large number leads to a deterioration of air quality, which in turn leads to an increase in complaints from the population. In order to counteract the adverse effects of chicken farming, malodorous air from poultry farms needs to be characterized using appropriate tools. This would give an idea of the degree and source of pollution in order to reduce the impact on the environment. This study aimed to test the ability of the developed e-nose based on six gas sensors to analyze odorous emissions from three poultry farms located in Meknes (Morocco) and Berlin (Germany). This pilot study was also carried out on odorous air samples in one week at different times of the day. Pattern recognition methods such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Support Vector Machines (SVM), and Discriminant Function Analysis (DFA) were used to process the dataset. Moreover, the gas sensors' sensitivity towards hydrogen sulfide, ammonia, and ethanol was also investigated. The finding results reveal that the developed system is able to differentiate the volume fractions of the analyzed gases. Furthermore, the relative humidity values have an effect of less than 1.6% on the gas sensor responses when the relative humidity increases from 15% to 67%. Data processing, using PCA, HCA, and SVM, shows clear discrimination between the odorous air samples collected from the three chicken farms, without any overlap with clean air. The same trend is obtained between odorous air samples collected at different days and times in a poultry farm using DFA and SVMs methods. From the relevant results, it can be concluded that the developed artificial sensory system can clearly classify and assess odorous air from poultry farms.