In this paper, we analyze applicability of F0 and cepstral features, namely LPCCs, MFCCs, PLPs for robust Automatic Gender Recognition (AGR). Through gender recognition studies on BANCA corpus comprising datasets of varying complexity, we show that use of voiced speech frames and modelling of higher spectral detail (i.e. using higher order cepstral coefficients) along with the use of dynamic features improve the robustness of the system towards mismatched training and test conditions. Moreover, our study shows that for matched clean training and test conditions and for multi-condition training, the AGR system is less sensitive to the order of cepstral coefficients and the use of dynamic features gives little-to-no gain. F0 and cepstral features perform equally well under clean conditions, however under noisy conditions cepstral features yield robust system compared to F0-based system.