In this paper, the author tests different hedonic and conventional quality adjustment methods in a uniform, but somewhat unconventional, descriptive framework. The main aim is to address questions on hedonic quality adjustment methods and their robustness in index compilation. The author does this by giving an empirical example with digital camera prices. The study shows how conventional quality adjusting methods may be treated parallel with hedonic ones and how these methods may be evaluated similarly with regression based methods. Contrary to structural models that many hedonic quality adjusted price indices are based on, the hedonic models in this paper are all used as forecast models that, the author believes, add to the robustness and practical utility of hedonics as a day-to-day tool for statistical agencies.