The prevalence of handwriting difficulties among school-aged children is around 10 – 30 %. Until now, there is no objective method to diagnose and rate developmental dysgraphia (DD) in Czech Republic. The goal of this study is to propose a new method of objective DD diagnosis based on quantitative analysis of online handwriting. For this purpose, we extracted a set of spatial, temporal, kinematic and dynamic features from three handwriting tasks. Consequently, we performed a correlation analysis between these features and score of handwriting proficiency screening questionaire (HPSQ), in order to identify parameters with a good discrimination power. Using random forests classifier in combination with quantification of alphabet writing task, we reached nearly 77% classification accuracy (75% sensitivity, 80% specificity). This pilot study proves the possibility of automatic DD diagnosis in children cohort writing with cursive letters.