Introduction. Wireless sensor networks can be used to solve various economic problems, including detection of objects (phenomena) of interest. Most of the well-known information processing algorithms in such networks are built according to a radial architecture. Such an approach assumes each sensor to have a direct access to the central node responsible for making the final decision. At the same time, this approach cannot always be implemented in practice, largely due to the complicated topography of the area. In this connection, the development of an integrated detection algorithm for sequential transmission of information from sensor to sensor is a relevant research task. This algorithm will contribute to improving the efficiency of decision making, extending the detection area and increasing the operation duration of the power sources of sensors.Aim. Synthesis and analysis of an integrated algorithm for object detection in wireless sensor networks with a linear topology.Materials and methods. The detection algorithm was synthesized based on the statistical theory of optimal signal detection and, specifically, on the following a priori information: the probability of errors in the detection of each sensor object and the probability of errors in communication channels. The efficiency of the synthesized algorithm was evaluated numerically in the MATLAB environment.Results. An algorithm for integrated detection of objects in wireless sensor networks was proposed. The efficiency of the developed algorithm was evaluated and the influence of such parameters as the signal/noise ratio and the number of sensors in the system on the detection efficiency was analyzed.Conclusion. The analysis of the synthesized algorithm can be performed with sufficient accuracy, with the algorithm parameters and the probability of errors when moving from sensor to sensor being determined by fairly simple recurrent expressions. Future research directions should address the influence of communication channels with fading and scattering on the detection efficiency, as well as the development of integrated detection algorithms with unknown target coordinates.