Current data processing applications use data with considerably less structure and much less precise queries than traditional database systems. The multimedia data, like images or videos, that offer query-by-example search, are a typical example. Such data can neither be ordered in a canonical manner nor meaningfully searched by precise database queries that would return exact matches. This novel situation is what has given rise to a similarity searching. The most general approach to the similarity search, still allowing construction of index structures, is modeled in metric space. Here an important issue is the efficiency - we need to achieve fast query response over huge volumes of data. During last two decades there have been developed many metric access methods and indexing structures, however, they mostly cannot scale up with the exponential growth of multimedia data volumes we encounter during last years. A way to compete this enormous growth is to design parallel and distributed solutions, either as an extension of the traditional centralized indexing techniques, or completely new ones, where the parallelism/distribution are inherent indexing properties. Hence, the goal of the proposed project is the design and implementation of parallel and distributed indexing techniques and comparison with existing centralized solutions.