In this study, the problem of developing simple dynamic models of an anaerobic digestion process is tackled using an identification procedure which proceeds in several consecutive steps. Starting from sets of experimental data describing the time evolution of several key component concentrations, i.e. biomass(es), substrates and products, the minimal number of macroscopic bioreactions required to represent the data at hand, as well as the parameters of the associated stoichiometry matrix, are determined using maximum likelihood principal component analysis. Then, the structure of the kinetic laws, together with their parameters, are identified using likelihood ratio tests to navigate through the branches of decision trees made of various kinetic structures. The effectiveness of the modelling procedure is illustrated with a simulated example of anaerobic digestion. As usual sensors only provide aggregate measurements of the component concentration, a deeper investigation of practical identifiability is achieved in relation with the influence of measurement errors.