A project has been undertaken to optimize the engine control software calibration of a modern heavy-duty diesel engine for operation with gas-to-liquids (GTL) diesel fuel, with the objective of developing an understanding of the scope for optimization with this fuel, which has different physical and combustion properties to that of conventional, crude-derived diesel. A data-driven, model-based calibration technique utilizing artificial neural networks was used to develop optimized transient and steady-state calibrations with both conventional diesel fuel, as well as neat GTL fuel. The engine control parameters that were optimized were injection timing, exhaust gas recirculation rate, rail pressure, and charge mass. The optimization aimed to minimize fuel consumption without deterioration in engine-out nitrogen oxide (NOx) and soot emissions. This paper reports on the calibration optimization methodology employed and the results achieved to date. These indicate that fuel efficiency can be improved by up to 3% with an optimized GTL calibration over a transient test cycle, and 2% over a steady-state test cycle, when compared to a conventional diesel fuel and the baseline engine calibration. Smaller efficiency improvements were also obtained with calibration optimization using the conventional diesel fuel. Efficiency improvements were primarily the result of reduced pumping losses which were enabled through reductions in the required charge mass. The resultant increase in soot emissions were offset by the lower inherent soot emissions of the GTL fuel. It was also found that the GTL fuel offers significantly greater scope for decreasing engine-out NOx emissions through optimization without compromising engine efficiency or soot emissions, than the conventional diesel fuel.