Abstract The extremely low flux of ultra-high energy cosmic rays (UHECR) makes their direct observation by orbital experiments practically impossible. For this reason all current and planned UHECR experiments detect cosmic rays indirectly by observing the extensive air showers (EAS) initiated by cosmic ray particles in the atmosphere. Various types of shower observables are analyzed in the modern UHECR experiments including a secondary radio signal and fluorescent light from the excited nitrogen molecules. Most of the data is collected by the network of surface stations which allows to measure the lateral EAS profile. The raw observables in this case are the time-resolved signals for the set of adjacent triggered stations. The Monte Carlo shower simulation is performed in order to recover the primary particle properties. In traditional techniques the MC simulation is used to fit some synthetic observables such as the shower rise time, the shower front curvature and the particle density normalized to a given distance from the core. In this talk we’ll consider an alternative approach based on the deep convolutional neural network trained on a large Monte-Carlo dataset, using the detector signal time series as an input. The above approach has proven its efficiency with the Monte-Carlo simulations of the Telescope Array Observatory surface detector. We’ll discuss in detail how the network architecture is optimized for this particular task.