Particle filtering is a widely used sequential methodology that approximates probability distributions by using discrete random measures composed of weighted particles. A large number of particles improves the quality of the approximation but increases the computational requirements. Although there exists an abundant variety of particle filtering algorithms in the literature, there is lack of work devoted to selecting or adapting the number of particles systematically. In this paper we propose a novel methodology for online assessment of convergence of particle filtering. Based on theoretical analysis of the assessment, we propose an algorithm for the adaptation of the number of particles in online manner. The performance of the proposed algorithm is demonstrated for two state-space models.