Early interactions of children with autism spectrum disorder (ASD) exhibit distinctive motor and object-manipulation features that may emerge within the first three years of life. This Master’s thesis presents a proof of concept for using instrumented toys and machine-learning algorithms for digital behavioral phenotyping and early screening of ASD. The project was conducted within the AutoPlay initiative, with the aim of providing clinicians with an objective decision-support system that integrates traditional assessments (e.g., ADOS-2) with quantitative, unobtrusive data. Unlike approaches based on wearable sensors or tablet interactions, the system employs a kit of commonly used toys equipped with inertial measurement units (IMUs) to record acceleration, angular velocity, and spatial orientation during free play in a naturalistic setting.