Neural networks simulations are notorious for being very time and resources consuming. However, although general purpose microprocessors have improved performance of these simulations, little is known on which microarchitecture features contribute the most to this performance improvement. In this context, the paper analyzes the performance impact of various microarchitectural mechanisms found in current superscalar microprocessors on the execution of a famous neural network the SOM algorithm. The conclusion is that SOM algorithm does not fully bene_t from the sophisticated hardware support existing in a state of the art super- scalar machine. It is especially true of the memory hierarchy as well as the branch prediction mechanisms.