Worldwide, cigarette smoking is one of the major causes of preventable death. A single cigarette contains more than a hundred toxins that have detrimental effects on the smoker himself and the people in his or her surroundings. Despite knowledge of these harms, smokers often struggle to quit. Accurate information on daily smoking might help for evaluating the smoking behavior of an individual and the effectiveness of related intervention process. Self-reporting, puff topography meters, and biomarkers are the primary tools available for the estimation of daily cigarette consumption. However, these methods have been proved to be either biased, inaccurate, obtrusive, or not suitable for all smokers. Thus, there was a need for the development of solutions for objective, accurate and automatic detection of cigarette smoking, especially under free-living conditions. This dissertation proposes new wearable sensor systems and related signal/image processing and pattern recognition methods for the objective, accurate and automatic detection of cigarette smoking with minimal effort from the person being observed. Main accomplishments of this dissertation are a) development and validation of a novel multi-sensory wearable system (Personal Automatic Cigarette Tracker v2 aka PACT 2.0) to facilitate studying the behavioral and physiological manifestations of cigarette smoking. The validation study involving forty participants suggests that this wearable system presents a reliable platform for collecting objective information on smoking behavior in the free-living; b) development and validation of a method to identify smoking events from the associated changes in heart rate parameters of the wearer. The proposed method also accounts for the breathing rate and body motion of the smoker to better distinguish these changes from intense physical activities. The validation study involving twenty participants suggests that these physiological parameters could be a useful indicator of cigarette smoking even in the free-living; c) validation of a wearable egocentric camera system to capture minute details of smoking events from the eye-level such as hand to mouth gestures during smoking puff, smoking environment, body posture or activities during smoking, etc. The human study involving ten participants demonstrates that this novel sensor system may facilitate the objective monitoring of cigarette smoking, categorizing smoking environment, and obtaining an overview of the smoking habit in free-living; d) development and validation of computer models to automatically extract behavioral metrics of cigarette smoking (such as smoking time of day, frequency, inter-cigarette interval, etc.) from images captured by the egocentric camera. The validation study performed on a large free-living image set shows the applicability of proposed models to extract an objective summary of daily smoking.