The mechanisms underlying neuronal coding and in particular the role of temporal spike coordination are hotly debated. However, this debate is often confounded by an implicit discussion about the use of appropriate analysis methods. To avoid wrong interpretation of data, the analysis of simultaneous spike trains for precise spike correlation needs to be properly adjusted to the features of experimental spike trains. In particular non-stationarity of the firing of individual neurons in time or across trials, a spike train structure deviating from Poisson, or a co-occurrence of such features in parallel spike trains, are potent generators of false positives. Problems can be avoided by including those features in the null-hypothesis of the significance test. In this context the usage of surrogate data becomes increasingly important, since the complexity of the data typically prevents analytical solutions. This review provides an overview of the potential obstacles in the correlation analysis of parallel spike data and possible routes to overcome them. The discussion is illustrated by referring to a concrete analysis tool (Unitary Events method) at every stage of the argument. The conclusions however, are of general nature and hold for other analysis techniques. Thorough testing and calibration of analysis tools is emphasized, also with respect to the impact of potentially erroneous preprocessing stages.