In this paper, we emphasize the use of diagnostics and visiometrics to obtain useful information from visualizations of complex physical phenomena. Our methodology revealed the fundamental characteristics of the vortex collapse problem. Preliminary work on feature extraction gave us a first insight into the type of problems that arise when dealing with large datasets. This first experience emphasized the importance of sharing tasks between the supercomputer and the workstation, where each machine may work more efficiently at different stages of the data processing. We have developed some tools to search fields for coherent objects, but in order to get a better understanding of the processes observed, new quantification tools are necessary. In particular we need to find measures of the identified structures that will allow us to describe them in a statistical manner. We expect this approach will lead to a model that will consider both the coherent structures observed and the random background in the turbulence flow field. An important extension of this work is feature tracking, which will characterize the vortex dynamics of the structures detected.