Aims: To consider temporomandibular joint (TMJ) anatomic interactions in order to refine hard tissue models differentiating (1) joints diagnosed with disc displacement with reduction (DDwR) or without reduction (DDw/oR) from asymptomatic joints (Normals), and (2) DDwR joints from DDw/oR joints. Methods:TMJ tomograms of 84 women with unilateral DDwR and 78 with unilateral DDw/oR were compared against each other and against those of 42 female Normal joints through the use of 14 linear and angular measurements, 8 ratios, and 34 interactions. A classification tree model for each comparison was tested for fit with sensitivity, specificity, accuracy, and log likelihood and compared to logistic regression models. Results: In the classification tree model comparison, the DDwR model versus the Normal model realized 35.9% log likelihood (88.0% sensitivity, 66.7% specificity); the DDw/oR model versus the Normal model realized 38.8% log likelihood (69.6% sensitivity, 85.7% specificity). The DDwR model versus the DDw/oR model realized 33.3% log likelihood (76.0% sensitivity, 73.1% specificity). In the logistic regression model comparison, the DDwR model versus the Normal model realized 40.8% log likelihood (82.1% sensitivity, 78.6% specificity) and the DDw/oR model versus the Normal model realized 61.1% log likelihood (85.9% sensitivity, 90.5% specificity). The DDwR model versus the DDw/oR model realized 21.5% log likelihood (60.3% sensitivity, 79.8% specificity). The addition of interactions to the logistic regression models improved the previously published log likelihood from 99% to 149%. Conclusion: The interactions improved logistic regression models and the data suggest that anatomic characteristics influence joint functional status. Because the models incorporated nearly all considered anatomic measurements, no anatomic factor is redundant in the closed TMJ biological system. Keywords: disc displacement with reduction, disc displacement without reduction, multifactorial analysis, temporomandibular joint anatomy