Summary: In most industrial applications and in the fields of scientific research, phenomena are highly nonlinear and/or they have high dimensionality. In such cases a model that describes exactly the phenomena is very hard to define, but often many simplified models describing the problem’s phenomenology if particular conditions are available. The problem of the multiphase flow rate estimation in oil extraction and transport processes fits into this class of problems. At present the most utilised approach to solving such a problem is that of comparing all the available models and techniques and then choosing the one that behaves better than the others in all different conditions. In our work we propose an approach in which all models are utilized with the task of getting a system that performs better than the best available model. In particular different mathematical models of multiphase flow rate estimation and neural models co-operate by using a meta-decision maker based on fuzzy theory. A discussion on new fuzzy decision models is carried out, and results on real data are shown.