Join processing on large-scale vector data is an important problem in many applications, as vectors are a common representation for various data types. Especially, several data analysis tasks like near duplicate detection, density-based clustering or data cleaning are based on similarity self-joins, which are a special type of join. For huge data sets, MapReduce proved to be a suitable, error-tolerant framework for parallel join algorithms. Recent approaches exploit the vector-space properties for low-dimensional vector data for an efficient join computation. However, so far no parallel similarity self-join approaches aiming at high-dimensional vector data were proposed. In this work we propose the novel similarity self-join algorithm PHiDJ (Parallel High-Dimensional Join) for the MapReduce framework. PHiDJ is well suited for medium to high-dimensional data and exploits multiple filter techniques for reducing communication and computational costs. We provide a solution for efficient join computation for skewed distributed data. Our experimental evaluation on medium- to high-dimensional data shows that our approach outperforms existing techniques.