Assessing human biomonitoring data often necessitates dealing with fragmentary prior knowledge and a complex set of variables. A procedure for explorative data analysis via decision-tree analysis was undertaken to obtain high-level descriptive summary information on human exposure on a timely basis. This study is based on a subset of monitoring data of the Environmental Specimen Bank for Human Tissues within the German Environmental Specimen Bank (n sigma: 2401: 42/58% males/females; 34/66% born in East/West Germany). Three well-known xenobiotic organochlorines (XOCs) [sum of polychlorinated biphenyls (PCBs) 138 + 153 + 180, pentachlorophenol (PCP), and hexachlorobenzene (HCB)] were used as target variables. Meta-data regarding the samples and individuals were collected via a self-reported questionnaire and used as potential predictor variables. Prior to decision-tree analysis, XOC levels were adjusted (trend, lipids, creatinine, total protein) via stepwise linear regression. Adjusted XOC levels were subsequently utilized to identify relevant predictors of human XOC exposure using Exhaustive CHAID as a common decision-tree algorithm. Although overall tree model quality is generally poor, consistent and plausible predictors for human exposure were identified. Besides time trend and clinical parameters, the predominant predictors for HCB and PCB exposure were birthplace, gender, age, body mass index (BMI), and consumption of milk/dairy products or animal fats. For PCP, predominant predictors were sampling site, gender, and consumption of animal fats. Summing results of decision-tree models and regression models, explained variances for metric scaled XOC are: PCB (34.2%) > HCB (30.3%) > PCP (17.2%). Explorative analysis of human biomonitoring data based on simple decision-tree analysis provides valuable information for planning further investigations and statistical data for analyses to support prediction, consequences, and regulation of XOC.