High-content data have the potential to inform mechanism of action for toxicants. However, most data to support this notion have been generated in vivo. Because many cell lines and primary cells maintain a differentiated cell phenotype, it is possible that cells grown in culture may also be useful in predictive toxicology via high-content approaches such as whole-genome microarray. We evaluated global changes in gene expression in primary rat hepatocytes exposed to two concentrations of ten hepatotoxicants: acetaminophen (APAP), β-naphthoflavone (BNF), chlorpromazine (CPZ), clofibrate (CLO), bis(2-ethylhexyl)phthalate (DEHP), diisononyl phthalate (DINP), methapyrilene (MP), valproic acid (VPA), phenobarbital (PB) and WY14643 at two separate time points. These compounds were selected to cover a range of mechanisms of toxicity, with some overlap in expected mechanism to address the question of how predictive gene expression analysis is, for a given mode of action. Gene expression microarray analysis was performed on cells after 24h and 48h of exposure to each chemical using Affymetrix microarrays. Cluster analysis suggests that the primary hepatocyte model was capable of responding to these hepatotoxicants, with changes in gene expression that appear to be mode of action-specific. Among the different methods used for analysis of the data, a combination method that used pathways (MOAs) to filter total probesets provided the most robust analysis. The analysis resulted in the phthalates clustering closely together, with the two other peroxisome proliferators, CLO and WY14643, eliciting similar responses at the whole-genome and pathway levels. The Cyp inducers PB, MP, CPZ and BNF also clustered together. VPA and APAP had profiles that were unique. A similar analysis was performed on externally available (TG-GATES) in vivo data for 6 of the chemicals (APAP, CLO, CPZ, MP, MP and WY14643) and compared to the in vitro result. These results indicate that transcription profiling using an in vitro assay may offer pertinent biological data to support predictions of in vivo hepatotoxicity potential.