Tonal music style is a substantial quality of the music material that can be recognized to belong to individual composers or their epoch. We were interested in the aspects of style that are detectable in notation. The objective of this study was to identify the main variables that allow for effective classification of tonal music styles. This was a quantitative study based only on measurable features giving rise to numerical continuous variables resulting from actual measurements carried out by software. As many features as practicable were operationalized and software routines devised to carry out the measurements on a purposely built database that stood for the whole range of styles of the period of common practice. The resulting dataset was investigated by machine learning algorithms. Key and rhythm measures and frequencies of use of scale degrees turned out to be the variables of highest predictive validity. The three key indicators were then used to effectively classify pieces by composer.