The performance of three black box models which were parameterized and validated using data collected from a laboratory scale fluidized bed anaerobic digester, were compared. The models investigated were all ARX (auto regressive with exogenous input) models, the first being a linear single input single output (SISO) model, the second a linear multi-input multi-output (MIMO) model and the third a nonlinear neural network based model. The performance of the models were compared using correlation analysis of the residuals (one-step-ahead prediction errors) and it was found that the SISO model was the least able to predict the changes in the reactor parameters (bicarbonate alkalinity, gas production rate and % carbon dioxide). The MIMO and neural models both performed reasonably well. Though the neural model was shown to be superior overall to the MIMO model, the simplicity of the latter should be a consideration in choosing between them. A simulation with an horizon approaching 48 h was performed using this model and showed that although the absolute values differed significantly, there were encouraging similarities between the dynamic behavior of the model and that of the fluidized bed reactor.