A parallel algorithm for solving distributed constraint networks (DCNs) is presented. The DCNs are composed of agents, each holding a local network of constraints, where all agents are connected by constraints. The parallel backtracking algorithm (PBT) initiates multiple search processes (SPs) that operate in parallel. Parallel search processes scan different parts of the search tree, each represented by a data structure that is circulated among the different agents. The PBT algorithm is presented in its simplest form generating search processes that are simple backtracking procedures. It is shown to be correct and complete and its implementation is streightforward. Preliminary experimental evaluation of the algorithm, on randomly generated DCNs, are presented. The degree of concurrency achieved in experiments on random DCNs is very encouraging. An interesting PBT implementation of a well known algorithm on DCNs (AWC ) is described, and demonstrated to have a high degree of concurrency also. Versions of PBT that generate parallel search processes that are more complex are described and left for future investigation..