Since the parameters of recognizers are estimated from training examples, it would be better to use the data that is collected from testing environments. However, collecting a large amount of data from testing environments to reliably estimate the parameters of recognizers is a very expensive task. In this research, a transformation approach based upon neural networks is studied to handle the training and testing condition mismatches. Neural networks can be used for situations where speech feature vectors are non-linearly distorted, such as in noisy reverberant speech or telephone speech. By using a neural network, the adaptation process requires a small amount of training data. First, a neural network is applied to the computation of an inverse distortion function. This type of network requires simultaneously recorded input and target pairs for training. Traditionally, neural networks are trained to minimize the mean squared error between the network output and the corresponding target value. However, minimizing the mean squared error does not guarantee maximum recognition accuracy. Therefore, a new objective function for the neural network is proposed, which makes use of the conditional probabilities that come from hidden Markov model (HMM) based recognizers. It maximizes the likelihood of the data from testing environments, and allows global optimization of the neural network when used with HMM-based recognizers. The new objective function can be used for the transformation of data, or for the adaptation of recognizers to an testing environment. In the latter case, the parameters of recognizers (i.e., mean vectors and covariance matrices) are transformed to best match the data distribution. The new algorithm is evaluated on a large vocabulary continuous speech recognition task.