In this paper, we present a study on the relationships between Chronic Lymphocytic Leukemia (CLL) related clinical attributes using network visualization and analysis techniques. This work is the first step of our long-term project to identify novel biomarkers for CLL, working in coordination with the NCI-funded CLL Research Consortium (cll.ucsd.edu). By computing Spearman correlation coefficients for 125 clinical attributes, we established an attribute network for CLL. Using network visualization techniques we identified a core network with peripheral nodes around it. An important observation is that many cytogenetic attributes are in the core network, indicating the connection between karyotypes (e.g., abnormality in Chromosome 17) and other clinical attributes. Since these karyotypes are linked to CLL etiology in many ways, the corresponding clinical attributes may be further selected and tested as markers for disease screening. The correlation analysis results are also consistent with previous studies using a knowledge engineering based approach for establishing relationships between such attributes.