Six degrees of freedom (6DOF) pose estimation is one of the common challenges in many robotic and computer vision applications. Most state of the art methods focus on conventional camera pose. In this paper, we propose to handle the problem of event camera pose estimation. We present in this paper to predict the camera pose using deep learning-based method. It is composed of a convolutional and a recurrent neural networks connected to a dense layer regressor. We present results from a set of convolutional neural networks including commonly used ones. We demonstrated the performance of the proposed method on several datasets. The results demonstrate the superiority of the proposed methods compared to state-of-the art methods.