The nitrogen (N) concentration and pH of culture media were optimized for increased fermentative hydrogen (H-2) production from the cyanobacterium, Synechocystis sp. PCC 6803. The optimization was conducted using two procedures, response surface methodology (RSM), which is commonly used, and a memory-based machine learning algorithm, Q(2), which has not been used previously in biotechnology applications. Both RSM and Q2 were successful in predicting optimum conditions that yielded higher H-2 than the media reported by Burrows et al., Int J Hydrogen Energy. 2008;33:6092-6099 optimized for N, S, and C (called EHB-1 media hereafter), which itself yielded almost 150 times more H-2 that? Synechocystis sp. PCC 6803 grown on sulfer-free BG-11 media. RSM predicted an optimum N concentration of 0.63 mM and pH of 7.77, which yielded 1.70 times more H-2 that? EHB-1 media when normalized to chlorophyll concentration (0.68 +/- 0.43 mu mol H-2 mg Chl(-1) h(-1)) and 1.35 times more when normalized to optical density (1.62 +/- 0.09 nmol H-2 OD730-1 h(-1)). Q2 predicted an optimum of 0.36 mM N and pH of 7.88, which yielded 1.94 and 1.27 times more H-2 than EHB-1 media when normalized to chlorophyll concentration (0.77 +/- 0.44 mu mol H-2 mg Chl(-1) h(-1)) and optical density (1.53 +/- 0.07 nmol H-2 OD730-1 h(-1)), respectively. Both optimization methods have unique benefits and drawbacks that are identified and discussed in this study. (D 2009 American Institute of Chemical Engineers Biotechnol. Prog., 25: 1009-1017, 2009