Identify Bad Products from given parameters using neural networks I have a problem at hand to identify Good/Bad Products using given parameters. The number of parameters are in the order of 5000s and there are multiple values for the parameters. However I do not have a labelled set of data which says these are the products that are good or bad.

For Example, Say the parameters are AX, AY, AZ, B, C, DX, DY, etc.
Each of them has a different range. Is decision trees the right approach? 

Can classification be applied to this problem?
No.  Classification requires labelled data.  Without labelled data there is no way to solve this.  How would you do anything at all, if you don't know which of the products in the training set are good and which are bad?  There's no basis for making a decision of any sort.