In this dissertation we propose a conditional pairwise pseudo-likelihood (CPPL) for parameter estimation in Markov random fields with Winsorized Poisson conditional distributions. The CPPL is defined as the direct product of conditional pairwise distributions corresponding to the pairs of random variables associated with the cliques of size two from the collection of spatial locations on a region of a lattice. Thus the CPPL is a modified version of Besag's pseudo-likelihood (PL) and Huang and Ogata's generalized pseudo-likelihood (GPL). We carry out calculations of the correspondingly defined maximum conditional pairwise pseudo-likelihood estimator (MCPPLE) for Markov random fields with Winsorized Poisson conditional distributions on the lattice. These simulation studies show that the MCPPLE has significantly better performance than Besag's maximum pseudo-likelihood estimator (MPLE), and its calculation is almost as easy to implement as the MPLE. Therefore, we suggest that for situations where each discrete local random variable conditional on its neighbors assumes more than two possible values, as in the Winsorized Poisson case, estimation based on the CPPL may be a computationally more feasible alternative than estimation based on Huang and Ogata's GPL.