This work investigates the use of meta-learning for optimization tasks when a classic operational research problem (flowshop) is considered at the base level. It involves sequencing a set of jobs to be processed by machines in series aiming to minimize the time spent. There are various algorithms or metaheuristics proposed to solve flowshop instances and the choice of the best one usually demands time and resources. Meta-learning applied to algorithm recommendation can simulate specialists’ choices as it provides a mapping between the problem characteristics (called meta-features) and the algorithm performance. This work proposes an approach for knowledge discovery operating on the performance of four metaheuristics (Hill Climbing, Tabu Search, Simulated Annealing, and Iterated Local Search) while solving several flowshop instances. Besides recommending the best metaheuristic for each instance, the proposed approach can also recommend well suited parameters values using an Irace-based training process. Despite the possibility of using complex meta-features and powerful machine learning technique, the first experiments have been conducted using simple low and high-level meta-features and a classic machine learning model called Classification and Regression Trees (CART) for the recommendation. Results show that the proposed approach is promising as the induced rules indicate that some metaheuristic parameters are preferable. Nevertheless, regarding the algorithm recommendation there is a lot of room for improvement.