While conventional malware detection approaches increasingly fail, modern heuristic strategies often perform dynamically, which is not possible in many applications due to related effort and the quantity of files. Based on existing work from [1] and [2] we analyse an approach towards statistical malware detection of PE executables. One benefit is its simplicity (evaluating 23 static features with moderate resource constrains), so it might support the application on large file amounts, e.g. for network-operators or a posteriori analyses in archival systems. After identifying promising features and their typical values, a custom hypothesis-based classification model and a statistical classification approach using the WEKA machine learning tool [3] are generated and evaluated. The results of large-scale classifications are compared showing that the custom, hypothesis based approach performs better on the chosen setup than the general purpose statistical algorithms. Concluding, malicious samples often have special characteristics so existing malware-scanners can effectively be supported.