This work puts forth a heuristic approach for investigating compromises between quality of fit and parameter reliability for the Monod-type kinetics employed to model microbial reductive dechlorination of trichloroethene. The methodology is demonstrated with three models of increasing fidelity and complexity. Model parameters were estimated with a stochastic global optimization algorithm, using scarce and inherently noisy experimental data from a mixed anaerobic microbial culture, which dechlorinated trichloroethene to ethene completely. Parameter reliability of each model was assessed using a Monte Carlo technique. Finally, an alternate quantity of applied interest was evaluated in order to assist with model discrimination. Results from the application of our approach suggest that the modeler should examine the implementation of conceptually simple models, even if they are a crude abstraction of reality, as they can be computationally less demanding and adequately accurate when model performance is assessed with criteria of applied interest, such as chloroethene elimination time.