Evidence that chemicals in the environment may cause developmental and reproductive abnormalities in fish and wildlife by disrupting normal endocrine functions has increased concern about potential adverse human health effects from such chemicals. US laws have now been enacted that require the US Environmental Protection Agency (EPA) to develop and validate a screening program to identify chemicals in food and water with potential endocrine-disrupting activity. EPA subsequently proposed an Endocrine Disruptor Screening Program that uses in vitro and in vivo test systems to identify chemicals that may adversely affect humans and ecologically important animal species. However, the endocrine system can be readily modulated by many experimental factors, including diet and the genetic background of the selected animal strain or stock. It is therefore desirable to minimize or avoid factors that cause or contribute to experimental variation in endocrine disruptor research and testing studies. Standard laboratory animal diets contain high and variable levels of phytoestrogens, which can modulate physiologic and behavioral responses similar to both endogenous estrogen as well as exogenous estrogenic chemicals. Other studies have determined that some commonly used outbred mice and rats are less responsive to estrogenic substances than certain inbred mouse and rat strains for various estrogen-sensitive endpoints. It is therefore critical to select appropriate biological models and diets for endocrine disruptor studies that provide optimal sensitivity and specificity to accomplish the research or testing objectives. An introduction is provided to 11 other papers in this issue that review these and other important laboratory animal experimental design considerations in greater detail, and that review laboratory animal and in vitro models currently being used or evaluated for endocrine disruptor research and testing. Selection of appropriate animal models and experimental design parameters for endocrine disruptor research and testing will minimize confounding experimental variables, increase the likelihood of replicable experimental results, and contribute to more reliable and relevant test systems.