Introducing integrated, automatic control to buildings operating with the environmental quality management (EQM) system, we found that existing energy models are not suitable for use in integrated control systems as they poorly represent the real time, interacting, and transient effects that occur under field conditions. We needed another high-precision estimator for energy efficiency and indoor environment and to this end we examined artificial neural networks (ANNs). This paper presents a road map for design and evaluation of ANN-based estimators of the given performance aspect in a complex interacting environment. It demonstrates that in creating a precise representation of a mathematical relationship one must evaluate the stability and fitness under randomly changing initial conditions. It also shows that ANN systems designed in this manner may have a high precision in characterizing the response of the building exposed to the variable outdoor climatic conditions. The absolute value of the relative errors ( M a x A R E ) being less than 1.4% for each stage of the ANN development proves that our objective of monitoring and EQM characterization can be reached.