Staphylococcus aureus (S. aureus), a vital nosocomial pathogen, is responsible for several diseases. With the increasing isolation rate in clinical specimens, rapid identification of this bacterial species is required. But present identification via conventional methods is time-consuming and lacks accuracy. The purpose of the current study was to evaluate the use of surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF MS) for rapid identification of S. aureus. A total of 120 clinical isolates of S. aureus and 153 non-S. aureus species were identified by conventional methods, and the species nature of all staphylococci was further confirmed by 16S rDNA sequencing. All strains observed were analyzed by SELDI-TOF MS. An identification model for S. aureus was developed and validated by an artificial neural network. The model based on 6 protein peaks exhibited a sensitivity of 98.4% and specificity of 98.6%. This strategy has the potential for rapid identification of S. aureus.