Many practical applications generate irregular, non-balanced divide-and-conquer trees which have different depths, possibly also different numbers of successors at different levels. Efficient parallelization is difficult as it requires dynamic partitioning and mapping of such trees to available processors. Irregular applications can obtain unpredictable intermediate results which then affect creation and termination of processes. The new proposed C++ framework called DAMPVM/DAC offers a combined scheme of dynamic process/data partitioning and migration which enables automatic parallelization of irregular divide-and-conquer applications taking into account processor speeds, network status, changing application requirements as well as external load introduced by other users. Experiments on a network of workstations include adaptive integration with and without process migration as well as static and dynamic codes for image recognition. The latter ones enable to assess both the overhead of the dynamic scheme compared to serial implementations for regular applications and scalability gains for non-uniform images.