Over the past decades, virtual reality (VR) technology has gained significant popularity and interest, both in research as well as on the consumer market. One promising application area of VR is virtual reality exposure therapy (VRET), which treats anxiety disorders by gradually exposing the patient to his/her fear using VR. To make VRET safe and effective, it is important to monitor the patient?s fear levels during the exposure. Non-invasive neuroimaging can be used to unobtrusively detect fear responses, among which functional near-infrared spectroscopy (fNIRS) technology exhibits the greatest potential for a combination with VR, due to its comparably low susceptibility to motion artifacts. This thesis aims to investigate to what extent the fNIRS signals captured from people with a fear of heights response and people without a fear of heights response during VR exposure differ, and to what extent a person?s fear of heights response to a VR environment can be detected using fNIRS data. Only a very limited amount of work has investigated how fear responses are reflected in fNIRS signals. Furthermore, no previous work on the automatic detection of fear responses using fNIRS data exists. The literature indicates that a combination of VR and fNIRS technology is feasible and that it allows for experiments with greater ecological validity than traditional lab experiments. An experiment was conducted during which participants with moderate fear of heights (experimental group, n=15) and participants with no to little fear of heights (control group, n=14) were exposed to VR scenarios involving heights (height condition) and no heights (ground condition). During the experiment, the participants' fNIRS signals were recorded. As an additional measurement, the heart rate (HR) of every participant was extracted from the fNIRS signals. Permutation tests were used to perform between-group statistical analyses and within-group statistical analyses (for the experimental group) on the fNIRS data and HR data. Furthermore, Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) were used to train and test subject-dependent classifiers and subject-independent classifiers on the data of the significant fNIRS channels of the experimental group, in order to detect fear responses. The between-group statistical analyses show that the fNIRS data of the control group and the experimental group are only significantly different in channel 3, where the grand average ?[HbO] contrast signal of the experimental group exceeds that of the control group. Furthermore, the HR data of both groups are not significantly different. The within-group statistical analyses show that there are significant differences between the grand average ?[HbO] values during fear responses and those during no-fear responses, where the ?[HbO] values of the fear responses were significantly higher than those of the no-fear responses in the channels located towards the frontal part of the pre-frontal cortex. Also, channel 23 was found to be significant for the grand average ?[HbR] signals. No significant differences were found between the HR data during fear responses or no fear responses of the experimental group. The subject-dependent SVM classifier using 1-second history of the fNIRS signals can detect fear responses at an average accuracy of 72.47% (SD 20.61). The subject-independent SVM classifier using 5-second history of the fNIRS signals can detect fear responses at an average accuracy of 77.29% (SD 10.64). The subject-independent classifiers show potential for usage in online detection scenarios, as they can be trained beforehand on existing fNIRS data and can classify the unseen data of a new person at an average accuracy above 75%.