Recently, several multi-layer perceptron (MLP)- based front-ends have been developed and used for Mandarin speech recognition, often showing significant complementary properties to conventional spectral features. Although widely used in multiple Mandarin systems, no systematic comparison of all the different approaches as well as their scalability has been proposed. The novelty of this correspondence is mainly experimental. In this work, all the MLP front-ends recently developed at multiple sites are described and compared in a systematic manner on a 100 hours setup. The study covers the two main directions along which the MLP features have evolved: the use of different input representations to the MLP and the use of more complex MLP architectures beyond the three-layer perceptron. The results are analyzed in terms of confusion matrices and the paper discusses a number of novel findings that the comparison reveals. Furthermore, the two best front-ends used in the GALE 2008 evaluation, referred as MLP1 and MLP2, are studied in a more complex LVCSR system in order to investigate their scalability in terms of the amount of training data (from 100 hours to 1600 hours) and the parametric system complexity (maximum likelihood versus discriminative training, speaker adaptative training, lattice level combination). Results on 5 hours of evaluation data from the GALE project reveal that the MLP features consistently produce improvements in the range of 15%–23% relative at the different steps of a multipass system when compared to mel-frequency cepstral coefficient (MFCC) and PLP features, suggesting that the improvements scale with the amount of data and with the complexity of the system. The integration of those features into the GALE 2008 evaluation system provide very competitive performances compared to other Mandarin systems. © 2011, IEEE.