Background and aims: Clinical predictive models for stroke recovery could offer the opportunity of targeted early intervention and more specific information for patients and carers. In this study, we developed and validated a patientspecific prognostic model for monitoring recovery after stroke and assessed its clinical utility. Methods: Four hundred and ninety-five patients from the population-based South London Stroke Register were included in a substudy between 2002 and 2004. Activities of daily living were assessed using Barthel Index) at one, two, three, four, six, eight, 12, 26, and 52 weeks after stroke. Penalized linear mixed models were developed to predict patients’ functional recovery trajectories. An external validation cohort included 1049 newly registered stroke patients between 2005 and 2011. Prediction errors on discrimination and calibration were assessed. The potential clinical utility was evaluated using prognostic accuracy measurements and decision curve analysis. Results: Predictive recovery curves showed good accuracy, with root mean squared deviation of 3 Barthel Index points and a R2 of 83% up to one year after stroke in the external cohort. The negative predictive values of the risk of poor recovery (Barthel Index <8) at three and 12 months were also excellent, 96% (95% CI [93.6–97.4]) and 93% [90.8–95.3], respectively, with a potential clinical utility measured by likelihood ratios (LRþ:17 [10.8–26.8] at three months and LRþ:11 [6.5–17.2] at 12 months). Decision curve analysis showed an increased clinical benefit, particularly at threshold probabilities of above 5% for predictive risk of poor outcomes. Conclusions: A recovery curves tool seems to accurately predict progression of functional recovery in poststroke patients.