Prematurely born infants receive special care in the Neonatal Intensive Care Unit (NICU), where various physiological parameters, such as heart rate, oxygen saturation and temperature are continuously monitored. However, there is no system for monitoring and interpreting their facial expressions, the most prominent discomfort indicator. In this paper, we present an experimental video monitoring system for automatic discomfort detection in infants' faces based on the analysis of their facial expressions. The proposed system uses an Active Appearance Model (AAM) to robustly track both the global motion of the newborn's face, as well as its inner features. The system detects discomfort by employing the AAM representations of the face on a frame-by-frame basis, using a Support Vector Machine (SVM) classifier. Three contributions increase the performance of the system. First, we extract several histogram-based texture descriptors to improve the AAM appearance representations. Second, we fuse the outputs of various individual SVM classifiers, which are trained on features with complementary qualities. Third, we improve the temporal behavior and stability of the discomfort detection by applying an averaging filter to the classification outputs. Additionally, for a higher robustness, we explore the effect of applying different image pre-processing algorithms for correcting illumination conditions and for image enhancement to evaluate possible detection improvements. The proposed system is evaluated in 15 videos of 8 infants, yielding a 0.98 AUC performance. As a bonus, the system offers monitoring of the infant's expressions when it is left unattended and it additionally provides objective judgment of discomfort.