Reliably measuring fear perception could help evaluate the effectiveness of treatments for pathological conditions such as specific phobias or post-traumatic stress syndrome (e.g., exposure therapy). In this study, we developed a novel vir-tual reality (VR) scenario to induce fear and evaluate the related physiological response by the analysis of skin conductance (SC) signal. Eighteen subjects voluntarily experienced the fear VR scenario while their SC was recorded. After the experiment, each participant was asked to score the perceived subjective fear using a Likert scale from 1 to 10. We used the cvxEDA algorithm to process the collected SC signals and extract several features able to estimate the autonomic response to the fearful stimuli. Finally, the extracted features were linearly combined to model the subjective fear perception scores by means of LASSO linear regression. The sparsification imposed by the LASSO procedure to mitigate the overfitting risk identified an optimal linear model including only the standard deviation of the tonic SC component as a regressor (p = 0.007; R2 = 0.3337). The significant contribution of this feature to the model suggests that subjects experiencing more intense subjective fear have a more variable and unstable sympathetic tone.