This work develops a filtering scheme for hybrid systems. The process dictating the configuration or regimes is a continuous-time Markov chain with a finite state space. Exploiting hierarchical structure of the underlying system, the states of the Markov chain are divided into a number of groups so that it jumps rapidly within each group and slowly among different groups. Focusing on reduction of computational complexity, the filtering scheme includes the following steps: ( 1) partition the state space of the Markov chain into subspaces, ( 2) derive a limit system in which the states are averaged out with respect to the invariant distributions of the Markov chain, ( 3) use the limit system to design quadratic variation test statistics, and ( 4) use the test statistics to identify which ergodic class the aggregated process belongs to and to construct near-optimal filter. For demonstration, a numerical example is presented.