The objective of this study was to design and evaluate the performance of a metal-oxide sensor-based electronic nose system (e-nose) for detecting Salmonella enterica in poultry manure. The system has hardware and software components for signal acquisition, data processing, and sample classification. An artificial neural network (ANN) model was used to classify manure samples as Salmonella-positive or Salmonella-negative. Seven manure samples were collected from different broiler houses and divided into four portions. Two portions were spiked with 10(3) and 2 x 10(3) CFU g(-1) of Salmonella enterica (ATCC 13311). The third portion was used for determining natural manure microflora, and the fourth portion was sterilized. All portions were incubated at 37 degrees C for 48 h. A total of 84 e-nose readings were recorded at different time intervals from the manure portions. A multilayer, feed-forward back-propagation ANN model was developed (training step) and validated with the e-nose readings. Of the 84 readings, 48 were used to develop the ANN model and the remainder was used to validate model performance. The model was able to classify the remaining 36 manure samples with an accuracy of 96%. In order to test the actual performance of the ANN model, 16 manure samples were collected from different barns and analyzed. The e-nose system was able to determine the Salmonella status of the manure samples with 100% accuracy.