The miniaturization of electronics and integrated signal processing coupled with huge data storage and fast data transmission are enabling the creation of a wide range of innovative sensors for biological systems. The changes in the underlying physiological process of the plants triggered by different environmental stimuli are resulting in electrical signals variation [Chatterjee, J. R. Soc. Interface, 2015]. Hence, the analysis of such signals could potentially lead to recognizing patterns in the electrical response that are induced by the stimuli. In this study, we are aiming to assess the potential of identifying the status of a plant (either a day/night cycle or a water deficit condition) employing signal processing techniques and advanced intelligent data analysis algorithms. Electrical signals from eight tomato plants (Admiro, Ailsa Craig flacca, and Ailsawild type) were recorded using the PhytlSigndevice (www.phytlsigns.com) in an Agroscope research station during one week.