Children's fine motor skills are linked not only to drawing ability but also to cognitive, social-emotional, self-regulatory, and academic development Suggate et al. "Journal of Research in Reading," 41(1), 1-19 (2018), Benedetti et al. (2014), Liew et al. "Early Education & Development," 22(4), 549-573 (2011), Liew (2012) and Xie et al. (2014). Current educators are assessing children's fine motor skills by either determining their shape drawing correctness Meisels et al. (1997) or measuring their drawing time duration Kochanska et al. (1997) and Liew et al. (2011) through paper-based assessments. However, these methods involve human experts manually analyzing children's fine motor skills, which can be time consuming and prone to human error or bias Kim et al. (2013) and Lotz et al. (2005). With many children using sketch-based applications on mobile devices like smartphones or tablets Anthony et al. (2012), computer-based fine motor skill assessment has the potential to address limitations of paper-based assessment by using automated measurements. In this work, we introduce a machine learning approach for analyzing aspects of children's fine motor skill development. We performed a study with 60 young children (aged 3 to 8 years old), and we implemented classifiers that determine children's age category based on features related to fine motor skill, predominantly for curvature- and corner-based drawing skills, surpassing the performance of our previous work Kim et al. (2013) and of human evaluators. We also present dedicated discussion and statistical testing of sketch recognition features which will further enhance automated fine motor assessment.