We demonstrate that NO2 can be quantitatively analysed in the presence of CO using a single tungsten oxide based resistive gas sensor. The working temperature of the sensor was modulated between 190 and 380 degrees C and its dynamic response to different concentrations of CO, NO2, and CO + NO2 mixtures was monitored. Either the fast Fourier transform (FFT) or the discrete wavelet transform (DWT) was used to extract important features from the sensor response. These features were then input to different (statistical and neural) pattern recognition methods. The species considered can be discriminated with a success rate higher than 90% using a Fuzzy ARTMAP or a radial basis function neural network. The concentrations of the gases studied can be accurately predicted, by using the DWT coupled to partial least squares (PLS) models. The correlation coefficients of the predicted versus real concentrations were 0.923, 0.870 and 0.866 for CO, NO2, and NO2 in CO + NO2 mixtures, respectively.