In this paper, we study a convolutional neural network we recently introduced in [9], intended to recognize 3D hand gestures via multivariate time series classification. The Convolutional Neural Network (CNN) we proposed processes sequences of hand-skeletal joints' positions using parallel convolutions. We justify the model's architecture and investigate its performance on hand gesture sequence classification tasks. Our model only uses hand-skeletal data and no depth image. Experimental results show that our approach achieves a state-of-the-art performance on a challenging dataset (DHG dataset from the SHREC 2017 3D Shape Retrieval Contest).Our model achieves a 91.28% classification accuracy for the 14 gesture classes case and an 84.35% classification accuracy for the 28 gesture classes case.