The foundation of flow cytometric data analysis is the graphic display and correlation of multiple list mode parameters, identifying locations of cell populations based on their location in n-dimensional space. The reproducibility of these methodologies are critical to their continued use as diagnostic, prognostic or therapeutic indicators. Our approach is based on the definition of reference space using predefined landmark cluster as internal standards. Unknown samples can then be conformed to this standard and a uniform analysis template applied. The results demonstrate that when using this approach, not only are the target populations brought into registration, but the minor populations are adjusted to consistent and predicable locations. This allows the creation of an analysis template that can be standardized across laboratories by accounting for both instrumentation and sample processing variation. The sample is analyzed in the following sequence: cluster finding algorithms are used to locate the landmark clusters in n-dimensional space, conformation equations are then applied to normalize data to the reference and then the analysis template applied to the conformed data. Additional discussion will be directed at the use of internal calibrators or landmark clusters for quantitative flow analysis. In the absence of landmark clusters a stabilized biological particle can be added and used as the internal calibrator.