Context • The benefits of a mindfulness meditation (MM) intervention are most often evidenced by improvements in self-rated stress and mental health. Given the physiological complexity of the psychological stress system, it is likely that some people benefit significantly, whereas others do not. Clinicians and researchers could benefit from further exploration to determine which baseline factors can predict clinically significant improvements from MM. Objectives • The study intended to determine (1) whether the baseline measures for participants who significantly benefitted from MM training were different from the baseline measures of participants who did not, and (2) whether a classification analysis using a decision-tree, machine-learning approach could be useful in predicting which individuals would be most likely to improve. Design • The research team performed a secondary analysis of a previously completed randomized, controlled clinical trial. Setting • The study occurred at the Oregon Health & Science University (Portland, OR, USA) and in participants' homes. Participants • Participants were 134 stressed, generally healthy adults from the metropolitan area of Portland, Oregon, who were 50 to 85 y old. Intervention • Participants were randomly assigned either to a 6-wk MM intervention group or to a waitlist control group, who received the same MM intervention after the waitlist period. Outcome Measures • Outcome measures were assessed at baseline and at 2-mo follow-up intervals. A responder was defined as someone who demonstrated a moderate, clinically significant improvement on the mental health component (MHC) of the short-form health-related quality of life (SF-36) (ie, a change ≥4). The MHC had demonstrated the greatest effect size in the primary analysis of the previously mentioned randomized, controlled clinical trial. Potential predictors were demographic information and baseline measures related to stress and affect. Univariate statistical analyses were performed to compare the values of predictors in the responder and nonresponder groups. In addition, predictors were chosen for a classification analysis using a decision tree approach. Results • Of the 134 original participants, 121 completed the MM intervention. As defined previously, 61 were responders and 60 were nonresponders. Analyses of the baseline measures demonstrated significant differences between the 2 groups in several measures: (1) the positive and negative affect schedule negative subscale (PANAS-neg), (2) the SF-36-MHC, and (3) the SF-36 energy/fatigue, with clinically worse scores being associated with greater likelihood of being a responder. Disappointingly, the decision-tree analyses were unable to achieve a classification rate of better than 65%. Conclusions • The differences in predictor variables between responders and nonresponders to an MM intervention suggested that those with worse mental health at baseline were more likely to improve. Decision-tree analysis was unable to usefully predict who would respond to the intervention.