BACKGROUND: Several tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless, identification of highly specific GO-terms in complex data sets is relatively complicated and the display of GO term assignments and GO enrichment analysis by simple tables or pie charts is not optimal. Valuable information such as the hierarchical position of a single GO term within the GO tree (topological ordering), or enrichment within a complex set of biological experiments is not displayed. Pie charts based on GO tree levels are, themselves, one-dimensional graphs, which cannot properly or efficiently represent the hierarchical specificity for the biological system being studied. RESULTS: Here we present a new method, which we name PCA2GO, capable of GO analysis using complex multidimensional experimental settings. We employed principal component analysis (PCA) and developed a new score, which takes into account the relative frequency of certain GO terms and their specificity (hierarchical position) within the GO graph. We evaluated the correlation between our representation score R and a standard measure of enrichment, namely p-values to convey the versatility of our approach to other methods and point out differences between our method and commonly used enrichment analyses. Although p values and the R score formally measure different quantities they should be correlated, because relative frequencies of GO terms occurrences within a dataset are an indirect measure of protein numbers related to this term. Therefore they are also related to enrichment. We showed that our score enables us to identify more specific GO-terms i.e. those positioned further down the GO-graph than other common tools used for this purpose. PCA2GO allows visualization and detection of multidimensional dependencies both within the acyclic graph (GO tree) and the experimental settings. Our method is intended for the analysis of several experimental sets, not for one set, like standard enrichment tools. To demonstrate the usefulness of our approach we performed a PCA2GO analysis of a fractionated cardiomyocyte protein dataset, which was identified by enhanced liquid chromatography-mass spectrometry (GeLC-MS). The analysis enabled us to detect distinct groups of proteins, which accurately reflect properties of biochemical cell fractions. CONCLUSIONS: We conclude that PCA2GO is an alternative efficient GO analysis tool with unique features for detection and visualization of multidimensional dependencies within the dataset under study. PCA2GO reveals strongly correlated GO terms within the experimental setting (in this case different fractions) by PCA group formation and improves detection of more specific GO terms within experiment dependent GO term groups than standard p value calculations.