Manual exploratory behaviors during object interaction that form the basis of tool use behavior, are mostly qualitatively characterized in terms of their frequency and duration of occurrence. To fully understand their functional and clinical significance, quantitative movement characterization is needed alongside their qualitative analysis. However, there are two challenges in quantifying them--(a) reliably classifying the type of movement and (b) performing this classification on a time series automatically. Here, we propose a machine learning-based classification method to address these challenges. We measured three common exploratory behaviors (object rotation, fingering, and throwing) in college-aged adults using "sensorized objects" that had wireless Inertial Measurement Units embedded in them. We then calculated several statistical features based on linear acceleration and angular velocity data to train machine learning classifiers to identify these behaviors. All classifiers identified the behaviors with a substantially higher accuracy (average accuracy = 84.95 ± 4.16%) than chance level (33.33%). Of all models tested, Support Vector Machine Quadratic, Support Vector Machine Medium Gaussian, and Narrow Neural Network were the best models in classifying the three behaviors (average accuracy = 89.34 ± 0.12%). This classification method shows potential for automating movement characterization of exploratory behaviors, thereby may aid early assessment of neurodevelopmental disorders.