The analysis of the food intake behavior has the potential to provide insights into the development of obesity and eating disorders. As an elementary part of this analysis, chewing strokes have to be detected and counted. Our approach for food intake analysis is the evaluation of chewing sounds generated during the process of eating. These sounds were recorded by microphones applied to the outer ear canal of the user. Eight different algorithms for automated chew event detection were presented and evaluated on two datasets. The first dataset contained food intake sounds from the consumption of six types of food. The second dataset consisted of recordings of different environmental sounds. These datasets contained 68,094 chew events in around 18 h recording data. The results of the automated chew event detection were compared to manual annotations. Precision and recall over 80% were achieved by most of the algorithms. A simple noise reduction algorithm using spectral subtraction was implemented for signal enhancement. Its benefit on the chew event detection performance was evaluated. A reduction of the number of false detections by 28% on average was achieved by maintaining the detection performance. The system is able to be used for calculation of the chewing frequency in laboratory settings.