Controllable sensing conditions provide the means for diversifying sensor response and achieving better selectivity. Modulating the sensing layer temperature of metal-oxide sensors is a popular method for multiplexing the limited number of sensing elements that can be employed in a practical array. Time limitations in many applications, however, cannot tolerate an ad-hoc, one-size-fits-all modulation pattern. When the response pattern is itself non-stationary, as in the transient phase, a temperature program also becomes infeasible. We consider the problem of determining and tuning into a fixed optimum temperature in a sensor array. For this purpose, we present an empirical analysis of the temperature's role on the performance of a metal-oxide gas sensor array in the identification of odorants along the response transient. We show that the optimal temperature in this sense depends heavily on the selection of (i) the set of candidate analytes, (ii) the time-window of the analysis, (iii) the feature extracted from the sensor response, and (iv) the computational identification method used.