Virtual Reality (VR) video games have become popular in recent years with the emergence of a wide variety of enhanced VR hardware systems. Typically, current VR video games incorporate high-quality auditory and video feedback along with vibro-tactile cues to provide an immersive experience. However, the ongoing VR hardware and software do not consider the state of the user to determine whether the video game is generating an enjoyable experience, or if it has become a stressing or boring experience. This work provides an assessment of stress level estimation from features extracted from Electrocardiography (ECG), Electrodermal Activity (EDA), and Electromyography (EMG) signals of users while playing a VR video game with different difficulty levels. Statistical differences were found between the rest and gaming stages for several extracted features. Regardless of the fact that no significant statistical differences between the three levels of difficulty were found by analyzing the EDA and ECG features, an 83.1% accuracy was obtained for the classification between the three levels with a KNN model. For the EMG signal, the obtained accuracy ranged between 99% and 100% for the distinction between a difficult level 1 and rest stage for all models. When all features from the ECG, EDA and EMG signals were used, an accuracy of 99% was obtained for the differentiation between the three difficulty levels and a resting stage. The presented work results may serve as a reference for future work regarding feature extraction and game difficulty adaptation for user experience enhancement.