The research presented here focuses on the general problem of finding tools and methods to compare and evaluate parallel architectures in this particular field: the computer vision. As there are several different parallel architectures proposed for machine vision, some means of comparison between them are necessary in order to employ the most suitable architecture for a given application. 'Benchmarks' are the most popular tools for machine speed comparison, but do not give any information on the most convenient hardware structures for implementation of a given vision problem. This paper tries to overcome this weakness by proposing a definition of the concept of a tool for the evaluation of parallel architecture (more general than a benchmark), and provides a characterization of the chosen algorithms. Taken into account different ways to process data, it is necessary to consider two different classes of machines: MISD and (MIMD, SPMD, SIMD) offering different programming models, thus leading to two classes of algorithms. Consequently, two algorithms, one for each class are proposed: 1) the extraction of connected components, and 2) a parallel region growing algorithm with data reorganization. The second algorithm tests the capabilities of the architecture to support the following: i) pyramidal data structures (initial region step), ii) a merge procedure between global and global information (adjacent regions to the growing region), and iii) a parallel merge procedure between local and global information (adjacent points to the growing region).