As the area of information visualization grows, a massive amount of visualization techniques has been developed. Consequently, the choice of an appropriate visualization has become more complex, usually resulting in unsatisfactory data analysis. Although there exist models and classifications that could guide the choice of a visualization technique, they are mostly generalist and do not present a clear methodology for evaluation and evolution. In contrast, we propose an annotation process for data visualization techniques based on an initial capability-driven collection of terms and concepts that encompasses visual components of both well established as well as modern visualization techniques. To demonstrate the initial collections expressiveness, we present a qualitative analysis of an experiment with specialist users at annotating visualization techniques from the D (Data-Driven Documents) library. Furthermore, to show the completeness of the collection, we automatically assess its coverage of all published papers from six major international information visualization conferences since 1995. Our results attest the expressiveness of the initial collection and its coverage of over 99% of the analysed literature. Finally, we discuss the limitations and alternatives for semi-automatically evolving the annotation process as new visualization techniques are developed and how the spread of this type of methodology could benefit the information visualization community. Keywords–Annotation Process; Data Visualization; Ontologies; Taxonomies.