This chapter highlights the current approaches in automated neonatal seizure detection and in particular focuses on classifier based methods. Automated detection of neonatal seizures has the potential to greatly improve the outcome of patients in the neonatal intensive care unit. The electroencephalogram (EEG) is the only signal on which 100% of electrographic seizures are visible and thus is considered the gold standard for neonatal seizure detection. Although a number of methods and algorithms have been proposed previously to automatically detect neonatal seizures, to date their transition to clinical use has been limited due to poor performances mainly attributed to large inter and intra-patient variability of seizure patterns and the presence of artifacts. Here, a novel detector is proposed based on time-domain, frequency-domain and information theory analysis of the signal combined with pattern recognition using machine learning principles. The proposed methodology is based on a classifier with a large and diverse feature set and includes a post-processing stage to incorporate contextual information of the signal. It is shown that this methodology achieves high classification accuracy for both classifiers and allows for the use of soft decisions, such as the probability of seizure over time, to be displayed.