In this paper we present our approach to automatically identify misogyny in Twitter tweets. That task is one of the two sub-tasks organized by AMI-IberEval 2018 organization. In order to carry out the task, we present a neural network approach. Neural network models have been demonstrated to be capable of achieving remarkable performance in sentence and document modeling. Convolutional neural network (CNN) and recurrent neural network (RNN) are two mainstream architectures for such modeling tasks, which adopt totally different ways of understanding natural languages. In this work we focus on RNN approach using a Bidirectional Long Short Term Memory (Bi-LSTM) with Conditional Random Fields (CRF) and we evaluate the proposed architecture on misogyny identification task (text classification). The experimental results show that the system can achieve good performance on this task obtaining 78.9 accuracy on English tweets and 76.8 accuracy on Spanish tweets.