The human olfactory system can classify new odors in a dynamic environment with varying odor complexity and concentration, while simultaneously reducing the influence of stable background odors. Replication of this capability has remained an active area of research over the past 3 decades and has great potential to advance medical diagnostics, environmental monitoring and industrial monitoring, among others. New methods for rapid dynamic temporal evaluation of chemical sensor arrays for the monitoring of analytes is explored in this work. One such method is high and low bandpass filtering of changing sensor responses; this is applied to reduce the effects of background noise and sensor drift over time. Processed sensor array responses, coupled with principal component analysis (PCA), will be used to develop a novel approach to classify odors in the presence of changing sensor responses associated with evolving odor concentrations. These methods will enable the removal of noise and drift, as well as facilitating the normalization to decouple classification patterns from intensity; lastly, PCA and artificial neural networks (ANNs) will be used to demonstrate the capability of this approach to function under dynamic conditions, where concentration is changing temporally.