Soft edit rules, i.e. constraints which identify (combinations of) values that are suspicious but not necessarily incorrect, are an important element of many traditional, manual editing processes. It is desirable to use the information contained in these edit rules also in automatic editing. However, current algorithms for automatic editing are not well suited to use soft edits because they treat all edit rules as hard constraints: each edit failure is attributed to an error.
Recently at Statistics Netherlands, a new automatic editing method has been developed that can distinguish between hard and soft edits. A prototype implementation of the new algorithm has been written in the R programming language. This paper reports some results of an application of this prototype to data from the Dutch Structural Business Statistics. The paper also introduces and tests several size measures of soft edit failures that can be used with the new automatic editing method.