The human adrenal cell line H295R constitutes a well-established model to evaluate potential alterations of steroidogenic pathways as a result of chemical exposure. However, to date most assays are based on the targeted investigation of a limited number of steroid hormones, thus preventing in-depth mechanistic interpretation with respect to steroidogenesis. In that context, analytical strategies coupling liquid chromatography and high-resolution mass spectrometry (LC-HRMS) have been reported as promising methods for an extended monitoring of steroid metabolites. However, unwanted sources of variability occurring during the acquisition process, including batch effects, may prevent relevant biochemical information to be properly highlighted. Dedicated data mining strategies are therefore needed to overcome these limitations, and extract relevant extended steroidomic profiles. The present study combines an untargeted LC-HRMS acquisition strategy with automated steroid metabolite annotation based on accurate mass and isotopic patterns, and a chemometric tool allowing the different sources of variability to be decomposed based on experimental design. This workflow was applied to the extended monitoring of steroidogenic dysregulations due to endocrine disrupting chemicals (EDCs) exposure in H295R cell cultures. A series of six chemicals, including acetyl tributylcitrate, octyl methoxycinnamate, torcetrapib, forskolin, linuron, and octocrylene, and dimethylsulfoxide as solvent control, were investigated through the simultaneous monitoring of 130 potential steroid metabolites, repeating the whole experiment independently three times. A stratified subsampling strategy was carried out to remove efficiently systematic batch variations and highlight subgroups of chemicals with similar steroid patterns. The proposed approach was reported as a potent screening strategy, as it allowed specific alterations of the steroid hormone biosynthesis and metabolism related to distinct mechanisms of action to be distinguished.