In this paper, the problem of $l_2-l_\infty$ filters design is addressed for sensor networks with missing measurements and switching topology. In a distributed filtering network, each local filter estimates the system states from not only its own information but also its neighboring filters’ information. Firstly, the missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. Meanwhile, the topology under consideration evolves by an event switch which is assumed to be subject to a Markov chain. Then, by constructing a Lyapunov functional, a sufficient condition is derived to guarantee the $l_2-l_\infty$ performance and the exponential mean-square stability of the resulting filtering error dynamics. Subsequently, the design method of the distributed $l_2-l_\infty$ filters is obtained through the linear matrix inequality(LMI) technique. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.