FuzzyCLIPS is a knowledge-base programming language designed especially for developing fuzzy expert systems. However, it usually requires much longer execution time than algorithmic languages. To address this problem, we propose to design a parallel version of FuzzyCLIPS to efficiently utilize the computing resources in emerging cluster and grid systems. MPI, the de-facto standard for parallel programming, is used to provide the facilities of parallelization and message passing. In contrast with previous researches using MPMD mode, we adopt SPMD programming model to ease the exploiting of data parallelism. Furthermore, to adapt to the heterogeneous computing resources, the FuzzyCLIPS inference engine is extended with a built-in function of load balancing to allocate appropriate amount of data to each process at the rum time. We have implemented a human resources Web site to evaluate the performance of the proposed parallel FuzzyCLIPS. The results show that we can garner superlinear speedup and provide a more reasonable response time.