This work describes the development of a low cost thermal desorption system to analyze a set of 31 exhaled breath samples previously acquired from CA and control patients (i.e. with gastritis and ulcer), which were concentrated using Tenax tubes in order to remove the moisture and trap the volatile compounds. The samples were stored at a temperature of 4°C for further analysis. The proposed system allowed that the volatile compounds were trapped inside the tubes to be extracted and sent to a measuring chamber with a gas sensor array sensitive to these compounds. The overall detection system composed of the measuring chamber, a high-precision power supply, advanced high-resolution data acquisition equipment and a computer that acquired and supervised the sensor responses. Once the information was acquired, different pre-processing (normalization) and data processing techniques such as: Principal Component Analysis (PCA), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM), were applied for the analysis and data classification of the exhaled breath. The thermal desorption system was able to extract the volatile compounds emitted from the breath, reducing the humidity of the samples to increase the selectivity, sensitivity and the performance of the system. A 99,44 % of the total variance by using PCA analysis was achieved and a 93.54 % of classification success rate using SVM was obtained.