A learning-based intelligent control system, the BioExpert, was developed and applied to the evaluation of multiparametric effects on iron oxidation by enrichment cultures of moderately thermophilic, acidophilic mining bacteria. The control system acquired and analyzed the data and then selected and maintained the sets of conditions that were evaluated. Through multiple iterations, the BioExpert selected sets of conditions that resulted in improved iron oxidation rates. The results obtained with the BioExpert suggested that temperature and pH were coupled, or interactive, parameters. Elevated temperatures (51.5°C) in combination with a moderately high pH (pH 1.84) impaired the growth of and iron oxidation by the enrichment culture. Moderate-to-high oxidation rates were achieved with a relatively high pH in combination with a relatively low temperature or, conversely, with a relatively low pH in combination with a relatively high temperature. The interactive effect of pH and temperature was not apparent from the results obtained in an experiment in which temperature was the only parameter that was varied. When the BioExpert was applied to a mixed culture containing mesophilic and thermophilic bacteria, the computer "learned" that pH 1.8, 45°C, and an inlet iron concentration from 30 to 35 mM were most favorable for iron oxidation. In conclusion, this study demonstrated that the learning-based intelligent control system BioExpert was an effective experimental tool that can be used to examine multiparametric effects on the growth and metabolic activity of mining bacteria.