In the context of recommendation methods, meta-learning considers the use of previous knowledge regarding problems solution and performance to indicate the best strategy, whenever it faces a new similar problem. This paper studies the use of meta-learning to recommend local search strategies to solve several instances of permutation flowshop problems. The method proposed to conceive the meta-learning model is described considering three main phases: (i) extracting the problem features, (ii) building the performance database and (iii) training the recommendation model. In this work, we extract instances features mainly through fitness landscape analysis, build the performance data using the Irace parameter tuning algorithm and train neural networks models for recommendation. The paper also analyzes two mechanisms that support the recommendation: one using classification as its basis and another considering ranking processes. Experiments conducted on a wide range of different flowshop instances show that it is possible to recommend not only the best algorithms, but also some of their suitable configurations.