In this paper, we present a cis-regulatory element based computational approach to genome-wide identification of genes putatively responding to various osmotic stresses in Arabidopsis thaliana. The rationale of our method is that gene expression is largely controlled at the transcriptional level through the interactions between transcription factors and cis-regulatory elements. Using cis-regulatory motifs known to regulate osmotic stress response, we therefore built an artificial neural network model to identify other functionally relevant genes involved in the same process. We performed Gene Ontology enrichment analysis on the 500 top-scoring predictions and found that, except for un-annotated ORFs ( approximately 40%), 91.3% of the enriched GO classification was related to stress response and ABA response. Publicly available gene expression profiling data of Arabidopsis under various stresses were used for cross validation. We also conducted RT-PCR analysis to experimentally verify selected predictions. According to our results, transcript levels of 27 out of 41 top-ranked genes (65.8%) altered under various osmotic stress treatments. We believe that a similar approach could be extensively adopted elsewhere to infer gene function in various cellular processes from different species.