Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the maximum likelihood method is computationally extremely expensive. We present simple new heuristics which yield accurate trees for synthetic (simulated) as well as real data and significantly reduce execution time. The new heuristics have been implemented in a program called RAxML which is freely available as open source code. Furthermore, we present a distributed version of our algorithm which is implemented in an MPI-based prototype. This prototype is currently being used to implement an http-based seti@home-like version of RAxML. We compare our program with PHYML and MrBayes which to our best knowledge are currently the fastest and most accurate programs for phylogenetic tree inference based on statistical methods. Experiments are conducted using 50 synthetic 100 taxon alignments as well as real-world alignments comprising 101 up to 1000 sequences. RAxML outperforms MrBayes for real-world data both in terms of speed and .nal likelihood values. Furthermore, for real data RAxML requires less time (factor 2-8) than PHYML to reach PHYML?s final likelihood values and yields better final trees due to its more exhaustive search strategy. For synthetic data MrBayes is slightly more accurate than RAxML and PHYML but signi.cantly slower.