It. is becoming increasingly clear that in the area of environmental systems analysis, where systems are often badly defined and models are subject to considerable uncertainty, recursive methods of time-series analysis can be particularly useful both in the initial and critical stages of system identification, as well as in the final stages of parameter estimation and model validation. Of the existing methods of time-series analysis, those based upon the method of instrumental variables are amongst the simplest and. at the same time, demonstrably most useful in practical applications. This paper discusses the instrumental variable (IV) and approximate maximum likelihood (AML) methods of recursive time-series analysis and shows how they can be unified to some extent within the context of maximum likelihood estimation. In this manner the virtues and limitations of the existing IV and AML techniques become more apparent and possible procedures for improving their statistical efficiency are exposed.