Skeletal muscle is one of the largest tissues in the human body. Changes in mRNA and protein abundance in this tissue are central to a large number of metabolic and other disorders, including, commonly, insulin resistance. Proteomic and microarray analyses are important approaches for gaining insight into the molecular and biochemical basis for normal and pathophysiological conditions. With the use of vastus lateralis muscle obtained from two groups of healthy, nonobese subjects, we performed a detailed comparison of the muscle proteome, obtained by HPLC-ESI-MS/MS, with the muscle transcriptome, obtained using oligonucleotide microarrays. HPLC-ESI-MS/MS analysis identified 507 unique proteins as present in four out of six subjects, while 5193 distinct transcripts were called present by oligonucleotide microarrays from four out of six subjects. The majority of the proteins identified by mass spectrometry also had their corresponding transcripts detected by microarray analysis, although 73 proteins were only identified in the proteomic analysis. Reflecting the high abundance of mitochondria in skeletal muscle, 30% of proteins detected were attributed to the mitochondrion, as compared to only 9% of transcripts. On the basis of Gene Ontology annotations, proteins assigned to mitochondrial inner membrane, mitochondrial envelope, structural molecule activity, electron transport, as well as generation of precursor metabolites and energy, had more corresponding transcripts detected than would be expected by chance. On the contrary, proteins assigned to Golgi apparatus, extracellular region, lyase activity, kinase activity, and protein modification process had fewer corresponding transcripts detected than would be expected by chance. In conclusion, these results provide the first global comparison of the human skeletal muscle proteome and transcriptome to date. These data show that a combination of proteomic and transcriptic analyses will provide data that can be used to test hypotheses regarding the pathogenesis of muscle disorders as well as to generate observational data that can be used to form novel hypotheses.