Named entity recognition is an intricate job that in generally requires a large amount of knowledge in the form of engineering and vocabularies to deliver high performance. This paper presents the implementation of a Named Entity Recognition (NER) system using the Long Short-Term Memory which can automatically identify word level and character level features using a bidirectional LSTM. The training set consists of approximately 12.74k wordforms, 5406 sentences and these sentences are manually annotated with six major named entity tags such as Person name, Location name, Organization name, Time, Art, and Miscellaneous name tags. The dataset is generated in the light of CoNLL dataset 2003. In the manually built corpus, we maintained the sequence to attain better accuracy. Our simulation result shows that the model achieves 98.37% accuracy in the training phase and provides a 74.99% F1 score with our own data set.