Diagnosis of a specific learning disability such as dysgraphia impacts children's academic progress and well-being. Dysgraphia is diagnosed by clinicians based on children's written product and educational staff's impressions. This process is time consuming and subjective. Consequently, many children with mild dysgraphia remain undiagnosed, especially those from lower socioeconomic backgrounds. In this work, a method for automatic identification and characterization of dysgraphia in third-grade children is described. The method is based on analyzing the child's writing dynamics by sampling the pressure the pen exerts on the paper as well as the pen's position and orientation by using a standard digital writing pad. Ninety-nine samples were collected from writers with dysgraphia and proficient writers. A wide range of features covering dynamic properties of the writing and typographic (i.e., visual) properties were extracted for each participant. Machine learning methodologies were used to infer a statistical model, which is capable of discriminating dysgraphic products from proficient products with approximately 90% accuracy. The model was analyzed to conclude which handwriting features are most discriminative. Since the model provides 90% sensitivity for a specificity of 90%, it is the first step toward future use as an effective standard indicator for dysgraphia detection.