Mutual interactions between heart rate, arterial blood pressure, and instantaneous lung volume have been previously described by parametric bivariate models, which only take into account two signals at a time. An expanded trivariate autoregressive model is introduced which is able to identify simultaneously all of the possible interactions among three signals, computing the transfer functions between each pair of signals. In addition, the model computes the autospectra and its parameters for each of the three signals. For comparison, simulations using simple sinusoidal signals with superimposed white noise and an example from a gradual tilting protocol with a random interval breathing technique were analyzed with both the bivariate and the trivariate techniques. As expected, differences were found between the bivariate and trivariate technique, for the gain values of the transfer function blocks, possibly because the bivariate technique is theoretically not able to separate the true interaction between two of the signals from the contributions of the third. Further application of the trivariate expanded technique could lead to a better understanding of specific physiological mechanisms or particular pathological and non-pathological phenomena occurring in the cardiovascular control system.