Breast cancer is a devastating disease that affects thousands of women every year influencing their psychological and physical well-being for many years after being diagnosed. The goal of the current study was to determine if there are distinct trajectories of functioning among breast cancer patients in the domains of negative psychological adjustment, positive psychological adjustment, and physical adjustment. This was accomplished using growth mixture modeling. Another goal of this study was to determine whether demographic, medical, and psychosocial variables were able to distinguish among the trajectories. The study combined women from two samples spanning 10 years providing a sample size of 376 women diagnosed and treated for breast cancer. These women were recruited to participate in a 10-week cognitive behavioral stress management intervention and were either randomized to the 10-week experimental condition or a one-day control group. It was hypothesized that distinct trajectories would emerge for each of the domains and that psychosocial variables (i.e., social support, benefit finding, and emotional approach coping) would distinguish among the trajectories. This study was able to statistically identify multiple classes or trajectories of adjustment, consistent with findings reported by Helgeson and colleagues (2004) and Donovan and colleagues (2007). It is difficult to say, however, whether these classes differ in clinically significant ways. The present study also provides a cautionary note to researchers who intend to use growth mixture modeling to identify different trajectories of functioning and the limitations associated with this statistical technique. First, it is important to start this process with strong empirical or theoretical support for the possibility of different classes or trajectories. Without this foundation it becomes difficult to justify why a certain number of classes were chosen. Another limitation of this statistical approach is that there is not a standard method for determining the best number of classes. There are conflicting opinions among researchers in the field about the best fit index to use when the multiple fit indices do not converge. A serious issue related to this is the fact that classes are used for interpreting results and drawing conclusions and inferences. Therefore, clinicians using GMM must be careful when deciding on the number of classes and the clinical inferences drawn from these analyses. Further research needs to be conducted validating these statistical techniques.