Soil quality is an essential issue in soil science research. It serves as a direct link between agricultural practices and sustainability. Soil quality is controlled by interactions of various physical, chemical and biological properties. In many soil science research projects, only certain aspects of the problem are covered, but when studying soil quality it is important to consider all physical, chemical and biological aspects. It is therefore important to be able to estimate various soil quality indicators. However, the most difficult step in the process of soil quality research is efficient data collection. Thus one of the challenges in developing soil quality indicators is the need for spatially dense data at reasonable cost. We address this problem by using a soil inference system. Given the measurements we know with a given level of certainty, we estimate the system soil quality indicators that we don’t know with minimal inaccuracy, by means of pedotransfer functions combinations. This paper will describe and demonstrate such a system. Using an example, we show that a simple system can be used to infer various soil physical and chemical quality indicators and their associated uncertainties, and how one soil property can be linked to others. Soil physical properties that can be inferred include soil hydraulic properties, thermal properties, mechanical properties, electrical properties, gas diffusion, and their interactions.