Particle filter (PF) based state estimation techniques have been proposed for numerous problems in robotics, computer vision, navigation etc. The accuracy of these algorithms depends on the number of particles employed to represent the probability density function, however, as the number of particles increases so does the computational cost of the algorithm, thereby limiting its usefulness in many real-time problems. Thus, there is always a trade-off between the required accuracy and computational efficiency in using such algorithms. This work implements a parallelized particle filter algorithm for multi-core processors to reduce the total processing time. The specific algorithm studied is the Monte Carlo Localization (MCL), a PF method for mobile robot localization. The multi-threaded version of MCL significantly improves the computational performance of the algorithm compared to its sequential execution. The results are compared with Amdahl's law which predicts the theoretical maximum speedup using multiple processors.The methodology used in this work can serve as a general framework for similar algorithms and applications.